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MIT Technology Review:西湖大学吴泰霖：用 AI 助力科学仿真和发现，可控权衡预测精度和计算效率
ICLR 2024 Spotlight for our paper “Compositional Generative Inverse Design”.
AAAI 2024 oral for our paper “Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution”
ICLR 2023 Spotlight for our paper “Learning Controllable Adaptive Simulation for Multi-resolution Physics”.
I have been organizing a bi-weekly Reading Group on Simulation and Inverse Design, with >20 participants (Ph.Ds, postdocs, assistant professor) from CS, EE, ME, and Energy departments from Stanford, MIT, and Caltech. Each time we spend 1h discussing a relevant paper deeply (Thursday 4-5pm PT, Friday 7am Beijing time). Contact me if you want to participate!
Invited talk at Lawrence Livermore National Laboratory (LLNL)’s DDPS seminar, “Learning to accelerate large-scale physical simulations”.
Guest lecture at Caltech CS159, “AI Physicist and Machine Learning for Simulations”
Organized “Deep Learning for Simulation” workshop in ICLR 2021
Our paper Toward an AI Physicist for Unsupervised Learning published at Physical Review E as part of a PRE Spotlight on Machine Learning in Physics.
Our paper Graph Information Bottleneck was featured in Synced AI Technology & Industry Review (机器之心).
Best Poster Award at ICML Time Series Workshop