2nd Physics Informed Machine Learning

This workshop continues discussions and explorations started in January 2016 at the first edition of the workshop. A revolution in statistics and machine learning (ML) is underway. Modern algorithms can now learn high level abstractions via hierarchical models, leading to breakthrough accuracies in benchmarks for computer vision, language, etc. Underlying these advances is a strong and deep connection to various aspects of statistical physics. For example, classical coarse graining concepts such as the renormalization group directly map to deep learning. Another connections physics inspired algorithms for accelerated graphical model inference; originally designed for simulations of lattice models of magnetism and quantum field theory, these algorithms have proven transformative in the training of complex, hierarchical ML models.

This workshop seeks perspectives on leveraging the deep connection between ML and physics, but now with the goal to better understand and model physical systems, static and dynamic. We invite experts both in machine learning techniques as well as domain science applications such as building reduced models for infrastructures (energy systems, traffic flows, etc), improving scale-reduced, large eddy modeling and simulations of turbulence that arise  in various mechanical-engineering, aerospace and  climate applications, reconstructing (from measurements and models) transport phenomena in complex materials, guiding development of inverse/design approaches in quantum physics, reconstructing network of functional relations in neurosciences and biophysics and designing new computational paradigms (such as related to quantum-, neuromorphic-, etc computers).

The workshop discussions are aimed towards approaches and methods for physical modeling applications where a big-data, black-box approach to ML is only a starting point. We seek participants who may suggest innovative approaches that extend application agnostic ML techniques by incorporating complex constraints imposed by physical principles (e.g. conservation laws, causality, symmetries, entropy principles and related).

The workshop format will include morning sessions on relevant machine learning methods, afternoon sessions on applications, late afternoon discussion and poster sessions.

We plan for active participation of LANL researchers and program managers across directorates and divisions interested in the physics informed learning. This emerging area of research has many aspects of computational co-design, and draws on LANL's strengths in statistical physics, theoretical and applied computer science, infrastructure modeling and simulations, fluids and materials modeling, and high performance computing. Looking forward, we view physics informed learning as a viable path for LANL and DOE toward truly predictive multi-scale modeling, which is a foundational challenge for mechanical, materials, computer, biological, and chemical engineering.

Machine Learning Topics:    

• Deep Learning and Neural Networks
• Graphical Model Learning
• Active and Reinforced Learning
• Adversarial & Robust Learning

Applications:

• Fluid Mechanics & Turbulence
• Materials & Quantum
• Physical Infrastructures (energy systems & transportations)
• New Computer Paradigms (quantum, neuromorphic, bio)
• Neuroscience and biophysics