Scientific Machine Learning Mini-Course
Video recordings are available below:
Differentiable Physics:
- February 24: Giuseppe Romano (MIT) (recording)
- March 3: Patrick Kidger (University of Oxford) (recording)
- March 10: Joe Greener (MRC Laboratory of Molecular Biology) (recording)
- March 17: Rafael Gomez-Bombarelli (MIT) (recording)
- March 24: Desmond Zhong (Siemens Technology) (recording)
- March 31: Taylor Howell & Simon Le Cleac'h (Stanford) (recording)
Symmetries, Physical Systems and Machine Learning:
Machine Learning Potentials and Force Fields for Materials Chemistry:
ML meets Information Theory and Statistical Mechanics:
ML for Fluid Dynamics:
Quantum Machine Learning:
ML-Embedded Physical Models:
ML Obeying Physical Symmetries:
Physics-Regularized ML: