Our lab's research solves the core and universal problems at the intersection of AI (deep learning) and scientific disciplines, including:
(1) AI for large-scale scientific simulations, design, and control (for fluid dynamics, aerospace engineering, mechanical engineering, materials science, life sciences) using generative models and foundation models;
(2) AI for scientific discovery (for life sciences and physics);
(3) Representation learning with graph neural networks, generative models, and information theory
Publications
Compositional Generative Inverse Design
We introduce a method that uses compositional generative models to design boundaries and initial states significantly more complex than the ones seen in training for physical simulationsRead More
Invited Talk
[2023/05]Graph Neural Networks for Large-scale Scientific Simulations, at 首届人工智能科学计算研讨会
[2023/03]Steps toward an AI scientist: neuro-symbolic models for concept generalization and theory learning, at AAAI 2023 Symposium of Computational Approaches to Scientific Discovery
[2023/03]AI + Science: motivation, advances and open problems, at Swarma (集智)
[2023/02]Learning structured representations for accelerating scientific discovery and simulation, at Yale Department of Statistics and Data Science
[2022/06]Learning to Accelerate Large-scale Physical Simulations in Fluid and Plasma Physics, at the Data-driven Physical Simulations (DDPS) seminar at Lawrence Livermore National Laboratory
[2022/04]Guest lecture at Caltech CS159, AI Physicist and Machine Learning for Simulations
[2024/02]Compositional generative inverse design, at AI4Science Talks
[2024/01]Machine learning for accelerating scientific simulation, design and control, at Annual Meeting of Peking University Center for Quantitative Biology
[2023/11]GNN for scientific simulations: towards adaptive multi-resolution simulators and a foundation neural operator, at LoG Shanghai Meetup
[2023/08]Machine learning of structured representations for accelerating scientific discovery and simulation, invited talk at CPS Fall Meeting（中国物理学会 2023 秋季会议）
[2023/08]Graph neural network for accelerating scientific simulation, at 2023 科学智能峰会
[2023/08]Phase transitions in the universal tradeoff between accuracy and simplicity in machine learning, at HK Satellite of StatPhys28
[2023/07]走向AI物理学家，做科学发现和仿真, at 果壳未来光锥
[2023/06]Machine learning of structured representations for accelerating scientific discovery and simulation, at Tsinghua University
[2023/06]Learning structured representations for accelerating simulation and design, at School of Intelligence Science and Technology, Peking University
[2023/05]Steps toward an AI scientist: neuro-symbolic models for concept generalization and theory learning, at Brown University Autonomous Empirical Research group.
[2023/04]Learning Controllable Adaptive Simulation for Multi-resolution Physics, at Stanford Data for Sustainability Conference 2023
[2023/03]Learning Controllable Adaptive Simulation for Multi-resolution Physics, at TechBeat
[2023/02]ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time, at AI Time
[2023/02]Graph Neural Networks for Large-scale Simulations, at Stanford HAI Climate-Centred Student Affinity Group
[2022/12]Steps toward an AI scientist: neuro-symbolic models for zero-shot learning of concepts and theories, at BIGAI
[2022/09]Learning to Accelerate Large-Scale Physical Simulations in Fluid and Plasma Physics, at SIAM 2022 Conference on Mathematics of Data Science
[2022/06]Learning to accelerate simulation and inverse optimization of PDEs via latent global evolution, Stanford CS ML lunch
[2021/12]Machine learning of physics theories, at Alan Turing Institute, Machine Learning and Dynamical Systems Seminar
[2021/10]Graph Information Bottleneck, at Beijing Academy of Artificial Intelligence (BAAI)
[2021/06]Machine learning of physics theories and its universal tradeoff between accuracy and simplicity, at Workshop on Artificial Scientific Discovery 2021
[2021/04]Phase transitions on the tradeoff between prediction and compression in machine learning, Stanford CS ML lunch
[2021/04]Machine Learning of Physics Theories, at Workshop on Artificial Scientific Discovery 2021, Summer school hosted by Max Planck Institute for the Science of Light
[2021/02]Phase transitions on the universal tradeoff between prediction and compression in machine learning, at Math Machine Learning seminar MPI MIS + UCLA
[2021/02]Machine Learning of Physics Theories, at Seminar Series of SJTU Institute of Science
[2020/10]Machine learning of physics theories and its universal tradeoff between accuracy and simplicity, at Los Alamos National Lab
[2020/02]Phase transitions for the information bottleneck, UIUC
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