Grounding foundation models for physical sciences

Despite the huge success of foundation models across fields, they still suffer from hallucinations and can produce physically inconsistent outputs. To leverage foundation models for physical sciences, it is critical to integrate first principles and physical laws to the learning and reasoning of these models. In this talk, I will discuss our on-going effort to ground foundation models, including diffusion models and large language models for physical sciences. In particular, I will discuss dynamics-informed diffusion models for emulating complex fluids and an adaptive framework for LLM agents to use scientific tools. I will demonstrate the use cases of our methods on applications in climate science and epidemiology modeling.