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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

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Publications

DiffPhyCon: A Generative Approach to Control Complex Physical Systems

We introduce a novel method for controlling complex physical systems using generative models, by minimizing the learned generative energy function and specified objectiveRead 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

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[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

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[2022/04] Guest lecture at Caltech CS159, AI Physicist and Machine Learning for Simulations

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[2024/04] Envisioning AI + Science in 2030, at 2050 Yunqi Forum

[2024/02] Compositional generative inverse design, at AI4Science Talks

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[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] 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] 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

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[2023/04] AI for scientific design: Surrogate model + backpropagation method, at Swarma

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[2023/03] Learning Controllable Adaptive Simulation for Multi-resolution Physics, at TechBeat

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[2023/02] ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time, at AI Time

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[2023/02] Graph Neural Networks for Large-scale Simulations, at Stanford HAI Climate-Centred Student Affinity Group

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[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

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[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

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[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

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[2021/02] Machine Learning of Physics Theories, at Seminar Series of SJTU Institute of Science

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[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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