AI for Scientific Simulation and Discovery Lab

[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] 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.

[2023/05] Graph Neural Networks for Large-scale Scientific Simulations, at 首届人工智能科学计算研讨会

[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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[2023/03] 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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[2023/03] AI + Science: motivation, advances and open problems, at Swarma (集智)

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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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[2023/02] Learning structured representations for accelerating scientific discovery and simulation, at Yale Department of Statistics and Data Science

[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

[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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[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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[2021/03] Learning to accelerate the simulation of PDEs, at CLARIPHY Topical Meetings

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[2021/02] Phase transitions on the universal tradeoff between prediction and compression in machine learning, at Math Machine Learning seminar MPI MIS + UCLA

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

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Talks

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

[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.

[2023/05] Graph Neural Networks for Large-scale Scientific Simulations, at 首届人工智能科学计算研讨会

[2023/04] Learning Controllable Adaptive Simulation for Multi-resolution Physics, at Stanford Data for Sustainability Conference 2023

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

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

[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] 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

[2023/02] Learning structured representations for accelerating scientific discovery and simulation, at Yale Department of Statistics and Data Science

[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

[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

[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

[2021/03] Learning to accelerate the simulation of PDEs, at CLARIPHY Topical Meetings

[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

Principal Investigator

Postdocs

PhD Students

Research Assistant

Visiting Students

Undergraduate

Collaborator

Alumnus

Principal Investigator

Postdocs

PhD Students

Research Assistant

Visiting Students

Undergraduate

Collaborator

Alumnus

PhD and Interns

Research Assistant

Postdocs

PhD and Interns

Research Assistant

Postdocs

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

[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.

[2023/05] Graph Neural Networks for Large-scale Scientific Simulations, at 首届人工智能科学计算研讨会

[2023/04] Learning Controllable Adaptive Simulation for Multi-resolution Physics, at Stanford Data for Sustainability Conference 2023

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

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

[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] 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

[2023/02] Learning structured representations for accelerating scientific discovery and simulation, at Yale Department of Statistics and Data Science

[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

[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

[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

[2021/03] Learning to accelerate the simulation of PDEs, at CLARIPHY Topical Meetings

[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