Generative Models for Physics-Based Control || Thermodynamics-Driven Learning || Feb 6, 2026

Value of the Information & Strength of the Argument

The talks provide valuable insights into cutting-edge research at the intersection of generative models and physics. The first talk demonstrates a novel method for controllable video generation with force prompts, supported by qualitative examples and a discussion of generalization. The argumentation is solid, acknowledging limitations and comparing with alternative approaches. The second talk introduces GIMLET, a principled framework for model discovery with thermodynamic guarantees, backed by benchmark results. Both presentations are well-structured and technically rigorous, with clear explanations of the underlying methods and their advantages.

The title accurately reflects the content: two talks on generative models for physics-based control and thermodynamics-driven learning.

8/10

The seminar presents two research talks with clear methodological descriptions, references to a preprint, and a Q&A session. The content is technical and appears scientifically sound, though it is not peer-reviewed in this format.

Markers derived by PSI from the transcript: the creator did not define chapters.

• 00:00Introduction to the seminar and first speaker Prof. Chen Sun.
• 02:00Prof. Sun introduces the concept of force prompts for video generation.
• 10:00Discussion on the limitations of physics simulators and the motivation for using video generative models.
• 30:00Prof. Sun presents results on generalizing force prompts to real-world scenarios.
• 1:00:00Introduction to the second speaker Prof. Suguru Shiratori and his talk on GIMLET.
• 1:10:00Prof. Shiratori explains the thermodynamic consistency and interpretability of GIMLET.
• 1:30:00Q&A session with questions about the generalizability and implementation of the proposed methods.

• GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics — Referenced by Prof. Shiratori as the preprint for his talk.

• GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics — The preprint provides the full details of the GIMLET framework.