It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Wednesday, August 26, 2026
WVU researcher says AI should disclose what it doesn’t know
Anthony Sicilia, assistant professor in the WVU Benjamin M. Statler College of Engineering and Mineral Resources, and student Voke Brume study why AI systems believe human users when they should know better, and how to build an AI model that can admit when it’s not sure.
Like humans, generative artificial intelligence doesn’t always know when it’s wrong, and a West Virginia University researcher is trying to get AI agents like ChatGPT to recognize — and admit — when that happens.
With more than $940,000 in National Science Foundation support, computer scientistAnthony Sicilia is examining why AI can become increasingly unreliable over the course of conversations with human users.
Artificial intelligence is already notorious for its tendency to “hallucinate,” or manufacture facts, but Sicilia is more interested in the technology’s tendency to be a people pleaser: appearing confident about information that is tenuous or accepting inaccuracies that a user provides.
An assistant professor in the WVU Benjamin M. Statler College of Engineering and Mineral Resources, Sicilia said that when a user questions an AI system’s responses, the AI often can’t figure out whether it made a mistake, the user is uncertain, or the conversation has shifted to a new objective.
“We’re interested in how misinformation develops over long conversations between an AI and a user,” he explained. “Those can become really messy in terms of reliability when the user starts providing information or context in addition to asking questions. When a user makes a suggestion, it can completely change the model’s confidence in an answer, even if the model was right to begin with.”
Sicilia sees AI’s “false confidence” as a particular problem for high-stakes fields like healthcare.
“One of the most concerning things about today’s AI systems is that they make mistakes in a very overconfident, trustworthy way,” he said.
“They speak fluently. They justify their answers. They use the tools of persuasion and rhetoric to convince you that they know what they’re talking about. Misinformation becomes a bigger problem when you have a system that can eloquently defend a point of view or an argument. That’s the crux of the problem we’re trying to solve — having AI systems be better at telling us when they’re not confident in an answer or don’t have the evidence to justify it.”
According to Sicilia, when users push back on accurate information, an AI system often defers and agrees in what’s referred to as “AI sycophancy.”
“It can happen in just three turns of the conversation,” he said. “The model proposes an answer that’s correct. The user says, ‘Well, I don’t think so.’ And the AI responds, ‘You’re totally right.’”
Users often fail to flag the logical flaws, he added, because the AI lacks the tells or signals that reveal when a human is lying or unsure.
Sicilia noted that humans often hypothesize their way through uncertainty, throwing out ideas to gauge whether they work. But the subtleties of that approach are lost on AI.
“We’re not cognitive scientists or linguists, but we try to pull as much from those disciplines as possible in our research about intelligence and reasoning,” he said. “One thing we think about is ‘theory of mind,’ or the understanding that each person has their own thoughts and feelings that may differ from our own.
“When you and I are having a conversation, theory of mind is what allows you to think about what I’m thinking about so you can best express what you want me to understand or get me to do what you need. That’s a big part of this research — the ability of AI systems to model what we’re thinking about while they’re working with us so they can be better collaborative partners because it’s not just about the AI system’s uncertainty, but about the user’s uncertainty as well. Conversationally, pushing back and questioning an answer is part of how humans learn and gain certainty,” Sicilia said.
He will scrutinize coding conversations between AI systems and novice programmers to measure the systems’ confidence calibration, evolving uncertainty, and the dialogue strategies the systems and humans employ.
Rather than treating AI confidence as static, he’ll study how conversational events like user disagreement, user suggestions, and shifts in topic alter a model’s confidence.
Sicilia’s goal is for an AI system to identify where uncertainty is coming from, tell a user why it doesn’t know an answer, or ask questions if a user provides information that seems incorrect. He said he wants to understand when confidence statements such as “I am 90% confident” are helpful to users, and when responses like “I am not sure” or “Can you clarify what you mean?” are better.
“One of the lenses I take to this research is from linguistics,” he said. “AI systems are increasingly language-based systems, so I look at conversations with them and think about the science of language. Humans use language to communicate, and AI systems are adopting that practice with varying degrees of effectiveness.
“When a system involves interactions with humans, that introduces a whole new variable. Our approach is a departure from current theories of the way machines learn, and we’re going to be collecting a lot of data to analyze how people who are not experts in a topic are using AI systems for that topic.”
In addition to relying on the public for research data, Sicilia will also create public-facing workshops and educational materials that teach students and workers how to identify unreliable AI answers, verify AI-generated code, and avoid AI overreliance.
WVU doctoral students Voke Brume and Louai Al Jabi are contributing to the research, along with undergraduate student Kaushika Wijerathne. Malihe Alikhani of Northeastern University is co-principal investigator.
“Despite how impressive AI systems have become, the public needs to understand that they are still imperfect tools — and that they can be wrong, sometimes in surprising ways,” Sicilia said. “That’s why we’re creating AI systems that respond appropriately when they lack evidence and communicate their uncertainty more honestly.”
New tool uses AI to help undergraduates think critically about research
An interdisciplinary team of researchers has developed and demonstrated a step-by-step framework called Socratic Challenger that uses artificial intelligence to help undergraduate students cultivate critical thinking skills. Specifically, the framework uses AI as a collaborator to develop research questions that can be pursued in laboratory or classroom settings.
“Formulating a good research question is a bottleneck in undergraduate inquiry,” says Aram Mikaelyan, corresponding author of a journal article on the work and an associate professor of entomology at North Carolina State University. “Students can name topics they’re interested in, but struggle to find ways to turn that interest into a meaningful research question that makes sense and can be developed into a research project.
“We also know that undergraduates are often turning to AI for assistance with academic work without having a clear idea of what is expected of them or what they are trying to accomplish, which is not helpful,” Mikaelyan says. “So we developed a detailed, step-by-step workflow that allows students to use AI tools, requires students to think critically, and helps students develop research questions that can be used to enable undergraduate research.”
“Ideally, students could work with mentors to learn how to develop a meaningful research question – but that’s not feasible given the number of students,” says Erin McKenney, co-author of the article and an assistant professor in NC State’s College of Agriculture and Life Sciences. “This framework, which we call Socratic Challenger, provides instructors with a new tool that can help to address that need.”
Socratic Challenger is a workflow consisting of eight steps, five of which are AI-enabled. The steps range from identifying a topic of interest to receiving feedback from (undergraduate) peers and instructors.
“To be clear, the AI is not generating the research question,” says Mikaelyan. “Instead, the AI is essentially being used to interrogate the student’s process and help them develop the habits of mind necessary to think critically about research. Is this really a gap in what we know about the topic? Is this question novel? What resources would I need to address this question, methodologically? And so on.”
To see whether Socratic Challenger was actually useful, the researchers conducted a proof-of-concept study with 45 students in an undergraduate ecology course. The students worked through the Socratic Challenger framework over a nine-week period and ultimately were tasked with submitting an abstract for the proposed research.
“We wanted to know if Socratic Challenger works and whether some steps in the workflow work better than others,” says McKenney. “We also wanted to know whether students feel like it makes a difference and, if so, how.”
“What we found is that the students who made use of the Socratic Challenger framework developed really thoughtful research questions,” says Mikaelyan.
The researchers also found that the step-by-step structure of Socratic Challenger was important, and every step in the workflow was deemed important by the students.
“Technology on its own doesn’t help student understanding,” says Dhvani Toprani, co-author of the paper and assistant director of learning design and support at Elon University. “But using technology as part of an intentional, step-by-step process did benefit the students’ ability to learn and think critically about research question development.”
“Basically, Socratic Challenger gives students the opportunity to have their ideas challenged earlier in the process of developing a research question,” says Olivia Mathieson, co-author of the paper and a Ph.D. student at NC State who participated in the initial testing of the workflow and discussions for classroom adaptation. “Learning how to think critically about their own ideas in a structured way is a valuable skill.”
“Socratic Challenger is iterative and open-ended, which makes it fairly generalizable to any unstructured, messy and complex learning environment,” says Toprani. “However, educational tools and educational research are always context dependent, and additional research is needed to broaden our understanding of how well this tool works and where it could make a positive difference.
“Could it be used in undergraduate courses in other disciplines? Could it be used to help students think critically about things other than how to develop a research question? Those are good questions, but more work would be needed to answer them.”
The Socratic Challenger: a structured GenAI-assisted workflow for undergraduate research inquiry
Article Publication Date
27-Aug-2026
Qualitative Ethics and the AI War Machine
Does AI shape decisions before we do? USF researcher questions what “human in the loop” really means
Research by the University of South Florida’s Lorien Jordan explores whether human oversight is meaningful when AI has already shaped the information reaching a decision-maker
TAMPA, Fla. (Aug. 26, 2026) -- Artificial intelligence is often discussed with a simple safeguard in mind: Keep a human in the loop. But what happens when AI is already shaping the information, options and recommendations that inform a decision?
That question is part of new research by Lorien Jordan, an assistant professor in the USF College of Education. In a recently published article, Jordan examines recent disputes involving major AI companies and the U.S. Department of Defense, challenging the idea that AI ethics can be reduced to choosing one company or platform over another.
The debate between Anthropic and the Defense Department over how its AI could be used illustrates the question: Even when a human remains responsible for the final decision, what happens when AI has already shaped the information behind it? Jordan's research looks at the broader systems that connect AI technologies to universities, publishers and researchers.
“One of the things that surprised me and that I will continue to research is how we always say we need a human in the loop when we use AI, but as we’re scaling decisions so quickly and so massively, what does it mean to be responsible for the decisions that follow if the information reaching a researcher has already been shaped by the systems behind an AI tool?”
Her research considers the larger systems behind AI tools and their “heritage” -- platforms such as ChatGPT and Claude are based on frontier large language models and built on shared research and techniques.
“I think awareness is an important aspect of AI,” she said. “Many debate whether one company appears more ‘responsible’ than another, but they are often overlooking the broader infrastructures that connect these technologies.”
The question extends beyond research, as AI is increasingly being used to assist decision-making in fields ranging from health care and education to business and national security.
For qualitative researchers like Jordan, the findings call for greater awareness of the systems in which AI research and use are already embedded. To further explore these concepts, she is in the process of developing a new study to analyze the choices researchers make with AI that will allow participants to see how AI interprets their words in real time.
Jordan’s work contributes to a growing body of research examining the ethical implications of AI and the larger systems and relationships that make these technologies possible.
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About the University of South Florida
The University of South Florida is a top-ranked research university serving approximately 50,000 students from across the globe at campuses in Tampa, St. Petersburg and USF Health. In 2025, U.S. News & World Report recognized USF with its highest overall ranking in university history, as a top 50 public university for the seventh consecutive year and as one of the top 15 best values among all public universities in the nation. U.S. News also ranks the USF Health Morsani College of Medicine in the highest tier, placing it as one of the top 16 medical schools in the nation and inside the top 10 among public universities. USF is a member of the Association of American Universities (AAU), a group that includes only the top 3% of universities in the U.S. With an all-time high of $750 million in research funding in 2025 and as a top 20 public university for producing U.S. patents, USF uses innovation to transform lives and shape a better future. The university generates an annual economic impact of nearly $10 billion for the state of Florida. USF’s Division I athletics teams compete in the American Conference. Learn more at www.usf.edu.
Today, anyone with a large enough artificial intelligence model can generate millions of new material designs in minutes. Unfortunately, that hasn’t led to a huge leap in the number of new materials being used to improve the performance of products like computer chips and rockets.
One reason for the translation gap is that current models don’t reliably factor in the chemical stability of the materials they generate, and unstable materials aren’t very useful in the real world. That forces industries to allocate huge computational budgets to screening out all the unstable materials they generate, in some cases leaving behind a tiny fraction of usable options.
Now, MIT researchers have developed a framework that can be applied at the beginning of the materials generation process to vastly improve the stability rate while achieving targeted material properties. It works by ensuring every design satisfies certain key rules of chemistry relating to the electrons around the materials’ atoms before the expensive generation step begins. The researchers call their approach “crystal generator with valence-constrained design, or CrysVCD.
In a paper published today in Nature Computational Science, the researchers show how CrysVCD allowed several commonly used material models to meet those valence shell rules more often, and used it to achieve high lattice-dynamics stability — a stringent stability test — in nearly 70 percent of computational material generations. They also showed the approach could support the creation of materials with specific desired properties, like high thermal conductivity or high dielectric constant, which is important for computer chips and data centers.
A hint of how the researchers envision people using their system is in the name.
“If material-generating models are like DVDs, we are like the DVD player,” says associate professor of nuclear science and engineering Mingda Li. “You can plug this into any kind of model, not only existing diffusion models but also future models, where people can’t generate enough stable materials, and it can improve stability.”
Joining Li on the paper are Mouyang Cheng SM ’26 and Weiliang Luo, MIT doctoral students in materials science and engineering and chemistry, respectively; Hao Tang PhD ’26, a recent graduate in materials science and engineering; Bowen Yu, a senior undergraduate in physics; Yongqiang Cheng, a staff scientist at the Oak Ridge National Laboratory; Weiwei Xie, an associate professor at Michigan State University; Ju Li, MIT’s Carl Richard Soderberg Professor in Power Engineering; and Heather Kulik, MIT’s Lammot du Pont Professor of Chemical Engineering.
More efficient materials
Computational approaches to materials design have been around for decades, but recent advances in artificial intelligence have increased excitement about their potential. Of particular interest are models that can start with a desired material property and work backward to deliver a material that achieves that goal.
Some of those models use an AI technique known as diffusion, which is commonly used to generate images, while others use large language models like the one powering ChatGPT and Claude, but both approaches struggle to ensure their material generations achieve chemical stability or follow fundamental principles about how chemicals interact and behave.
The solution has been to add another layer of computing on top of the generative process to filter out unstable materials.
“It’s becoming easy to generate the material structure,” Cheng says. “But the validation process, especially the part where you test the stability, has a huge computational cost. It’s something like 90 percent of the computational cost for creating usable materials, and it can take weeks or months.”
Big companies with huge computing budgets can afford to run those processes, but many small companies and research labs can’t, potentially limiting innovation in the field.
“In academia, where we have fewer resources, I think we can still achieve strong performance with smarter designs and other approaches,” Kulik explains. “Generating a model and then down-selecting for stability is inefficient. There’s a high computational cost. But if we put a language model in the beginning of the process to constrain the generation, you can significantly enhance the ratio of stable materials generated.”
The new study involved MIT researchers affiliated with the departments of Materials Science and Engineering, Chemistry, Chemical Engineering, Physics, and Nuclear Science and Engineering. Together the researchers combined AI diffusion models with a language model. In the first stage of their process, the language model produces chemically valid formulas. In the second stage, the diffusion model uses that formula to generate the corresponding atomic structure of the crystal material in coordination with the underlying material generation model.
“Diffusion for typical material generation is a slow process — you can think of it like 1,000 steps to create one material,” Luo says.
“In contrast, when our model is used in the beginning, you can think of it like five steps. It allows you to screen out the unstable materials to generate higher quality materials. And it works with any models generating materials,” Tang adds.
The researchers showed their approach created more stable materials an order of magnitude more efficiently than approaches that rely on screening materials after they’re generated. When fine-tuned on stability metrics, their approach produced crystalline materials that achieved 68 percent mechanical stability and 85 percent metastability, which measures if a material stays in a stable state when undisturbed.
The researchers then used their approach to generate material candidates with high thermal conductivity and easy polarization in an electric field.
“These are materials useful for the semiconductor industry and high thermal conductivity materials relevant to data center cooling,” Ju Li says. “In principle, you could also use this to create other properties, but thermal conductivity has become really important for cooling data centers. There’s been a huge increase in energy use in that industry, and 30 percent of that energy goes to cooling. The industry needs materials with high thermal conductivity to more efficiently remove the heat.”
Democratizing material design
The new approach doesn’t work with every kind of material — it works best with solid structures with highly ordered internal arrangements. Still, the approach could be used to generate stable new crystalline materials with a host of important properties.
“We are not just generating stable materials, we’re also prioritizing performance,” Cheng says. “Any time you have two goals, achieving those goals with anything over 50 percent is hard in this field. In the past, people might have a goal for specific properties and not stability, or vice-versa, and get a single-digit percentage of materials that fit their goal.”
Ultimately the approach will enable more researchers to develop novel materials for a range of next-generation applications.
“This will save huge computation costs and time by removing downstream selection requirements,” Li says. “That will help not only large efforts that generate hundreds of millions of materials, but also smaller research groups with targeted applications.”
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The work was supported, in part, by the U.S. Department of Energy, a Mathworks Engineering Fellowship, the National Science Foundation, and the U.S. Defense Threat Reduction Agency.
Enhancing materials discovery with valence-constrained design in generative modeling
Article Publication Date
26-Aug-2026
Scripps Research and collaborators awarded $19.5 million to establish an open-access autonomous chemistry laboratory
The U.S. National Science Foundation award will support development of an AI-enabled, remotely accessible lab to serve as both a discovery engine and training tool for a new generation of chemists.
LA JOLLA, CA—Recent advances in robotics, automation and artificial intelligence (AI) have the potential to transform the process of scientific discovery. At Scripps Research, these technologies are applied to chemistry through the Automated Synthesis Facility, which provides robotic systems, analytical instrumentation and dedicated staff to elevate synthetic chemistry research within the institute and beyond.
Now, a new award from the U.S. National Science Foundation (NSF) totaling $19.5 million over four years will expand these capabilities by supporting a collaborative effort among Scripps Research, UCLA and Sunthetics (an AI-for-chemistry company) to establish a nationally accessible, fully automated chemistry laboratory. This “Chemistry Node” will be one of 20 AI-enabled NSF Test Bed: Toward a Network of Programmable Cloud Laboratory (NSF PCL) nodes across the United States, which will test, scale and demonstrate new methods and tools that advance automated science and engineering, driving discoveries across a wide range of fields. The NSF PCL initiative is led by the NSF Directorate for Technology, Innovation and Partnerships, NSF’s newest Directorate in more than 30 years.
“This award gives us an exciting opportunity to approach synthetic chemistry and its quirks as a data science problem, with the help of some of the field’s brightest minds here at Scripps,” says Brandon Orzolek, the lead principal investigator on the project and the scientific director of the Automated Synthesis Facility at Scripps Research. “The team unites experts in automation, data and computer science, synthetic chemistry, and AI to innovate how we produce, handle, store and share chemical information. With this support, we have a real shot at unearthing data-driven insights that strengthen our understanding of reproducible and transferable chemistry, something that may ultimately redefine the way we design new chemical reactions.”
The Chemistry Node, housed at Scripps Research’s Automated Synthesis Facility, aims to advance reaction discovery and optimization by translating researcher ideas into executable experiments, running them on robotic infrastructure, agentically analyzing the results with analytical instrumentation in real time, and then using AI to propose the next set of experiments.
One chemistry problem that the node will tackle is optimizing processes for creating specific organic small molecules, which are often used in medicines, agricultural chemicals and building blocks for new materials. Some of the most promising newer methods for making these molecules require multiple catalytic cycles—like a set of interconnected gears that must spin at well-matched rates to get from input to output. It can be cumbersome to figure out how changing one variable will affect each cycle and their ability to work together. Automation can help make the process of tweaking and assessing these variables, in order to streamline the overall reaction, much more efficient.
“Over the four-year award term, our node will progress through multiple phases, increasing accessibility to the broader community along the way,” says Orzolek. “By the fourth year, we should have a closed-loop system that can take an idea from a user anywhere in the world and turn it into an end product, expediting science that would otherwise take years to complete.”
In addition to being a discovery engine, the node will enhance education, the program leaders say. The node will provide hands-on learning for students from various institutions, including R2 universities (which have high research activity, but not as high as R1 universities), primarily undergraduate institutions (PUIs), and two-year colleges that may not otherwise have access to this type of advanced research infrastructure.
“From a training perspective, this provides a really unique opportunity to educate a new generation of chemists, who aren’t only at the bleeding edge of synthetic chemistry and catalysis, but also have AI and automation research built into their experience,” says co-principal investigator of the project Keary Engle, who’s also a professor and the John and Susan Diekman Dean of Graduate & Postdoctoral Studies at Scripps Research. “This will be an incubator to cultivate a new type of chemist that we think will drive the field forward in the future.”
Members of Engle’s lab will be among the first to propose experiments and test drive the program.
Orzolek is joined by several co-principal investigators. Engle will lead catalytic experimental design; Abigail Doyle, a professor at UCLA, will direct data-rich experimental design, quality control and academic tool integration; and Daniela Blanco, CEO of Sunthetics, will lead AI integration and web interface development.
“This new program offers a chance to address test cases that are challenging to optimize the old-fashioned way, manually, vial by vial,” says Engle. “We want the node to be so nimble and forward-thinking that it can map out a whole optimization workflow to the final useful data output. That output could be a synthetic method we can deploy to solve particular problems, like making a promising drug candidate or developing a new way to connect existing building blocks.”
For Orzolek, the project marks an important personal milestone.
“This award touches everything that I’ve ever been interested in throughout my training,” says Orzolek. “It addresses the need for reproducible and consistent data, it’s at the forefront of automation, machine learning and data science, and perhaps most importantly to me, broad access to this infrastructure will help educate a new class of automation-informed chemists from many different backgrounds.”
HydroGym trains, assesses AI for actively controlling fluid dynamics
With more than 60 environments, the simulated proving ground aims to speed the development of AI controllers that optimize drag, lift, noise and heat management
Controlling the flow of fluids is critical to many fields of science and engineering, but with complex physics and many variables, these flows usually can't be directly predicted.
By demonstrating the success of a drag-reducing strategy trained in a simple scenario and applied to a simulated airplane wing, the team showed the potential for HydroGym to advance broadly applicable flow control models.
The collaboration includes the University of Washington, University of Michigan, RWTH Aachen University and the Technical University of Munich.
A platform for training and comparing machine learning models for actively reducing drag, improving lift, cutting noise and managing heat has been launched by an international team including researchers at the University of Washington, University of Michigan Engineering, RWTH Aachen University and the Technical University of Munich.
"Fluid flows are central to several trillion-dollar industries, including energy, transportation, health and defense. An improved ability to understand and control these flows could have an immense economic and ecological impact, helping us to enable a better future," said Steven Brunton, senior co-corresponding author of the study in Nature and the Boeing Professor in AI & Data-Driven Engineering within UW mechanical engineering.
The large number of variables typically makes it impossible to directly calculate fluid behaviors in realistic scenarios. Now, the international team has built a platform focused on solving this problem through reinforcement learning, a form of machine learning that has already revolutionized fields like protein folding and nuclear fusion by training AI agents through interactions with their environments.
Proving ground for reinforcement learning controllers
By incorporating physics knowledge into the training of AI agents that actively modify fluid flows over surfaces, the new platform reduced the amount of trial-and-error needed to optimize reinforcement learning control strategies by as much as 65%. Called HydroGym, it also compares control strategies on a level playing field, helping identify the best available approaches for solving problems such as improving the efficiency of airplanes and wind turbines, making jet engines quieter and cooling supercomputers.
"I hope this helps move the field from individual demonstrations towards a more systematic and collaborative approach to discovering general principles for controlling complex flows," said Christian Lagemann, first author of the study and former postdoctoral researcher at UW under Brunton, the HydroGym principal investigator.
"Instead of developing controllers for isolated flow problems with no common framework for comparison, we can now study how control strategies transfer across different geometric shapes and types of flow, or train in inexpensive surrogate environments and test in much more realistic scenarios."
HydroGym focuses on training and testing active methods for controlling fluid flows, such as shape morphing, tiny flaps or spinning elements, or systems of jets that counter or redirect turbulence. Its development was primarily funded by the U.S. National Science Foundation and the Boeing Co., with additional funding from the University of Michigan and others.
From simple flows to realistic applications
In one demonstration, the team explored how a channel formed of two flat surfaces, peppered with holes like air hockey tables, could keep surface friction to a minimum. They trained a machine learning model to control the air entering and exiting the holes, disrupting the turbulent flows that increase friction while always keeping the incoming and outgoing air in balance.
The researchers then applied the controller to a much more complex scenario: a section of a simulated airplane wing. It reduced the surface friction across the wing by 38% and the overall drag by 11%, while training in a simpler scenario was 100 times faster and 10,000 times cheaper than training directly on the wing.
"One of the key findings is zero-shot transfer: in other words, learning in simple geometries to distill the key physics, and deploying the models in very complex geometries with very high control performance," said Ricardo Vinuesa, co-corresponding author of the study and a U-M associate professor of aerospace engineering.
He served as co-principal investigator of the HydroGym project with Wolfgang Schröder, professor of fluid mechanics at RWTH Aachen, and Nikolaus Adams, professor of aerodynamics and fluid mechanics at TUM.
The ability to apply the model to a new situation without additional training indicates the potential for progress toward a single model of fluid dynamics—one that captures enough physics to apply to different levels of turbulence, on any surface shape, and to liquid and gas flows or even a mixture of the two.
More than 60 environments for training and comparison
HydroGym is not restricted to models that run as one central "brain." It can also train and test distributed control systems in which individual controllers manage regions of a surface while coordinating with their neighbors. This is important because on large surfaces, there is too much information for a centralized model to manage. A system of smaller controllers, trained through multi-agent reinforcement learning, takes advantage of the fact that though the flow may differ in time and space, it follows the same rules over the whole surface.
Rather than drawing from historical datasets for model training, HydroGym generates simulated datasets on the fly, enabling users to choose from multiple physics modeling strategies, including lattice Boltzmann, finite-volume, spectral-element and finite-element. Because several of these solvers—including JAX-Fluids—support automatic differentiation, they can be embedded directly into HydroGym's training loop, opening the door to gradient-based and hybrid optimization strategies alongside standard reinforcement learning.
The team prepared more than 60 testing environments—with various control strategies, surfaces and flows—in which HydroGym users can train their models and compare them against the competition. HydroGym's code, documentation and full set of environments are freely available on GitHub, and the team is actively growing the platform together with the wider fluid-dynamics community.
The team includes researchers from Inha University, South Korea; KTH Royal Institute of Technology, Sweden; HESAM University, France; Mediatek Research, U.K.; and the German Center for Neurodegenerative Disease.
Adams is also director of the Munich Institute of Integrated Materials, Energy and Process Engineering. Schröder is also dean of mechanical engineering at RWTH Aachen.
Additional funding was provided by the U.S. Army Research Office, the German Research Foundation and the European Research Council.
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