Conference Tools for 2021 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title	Parsimonious Neural Networks Learn Classical Mechanics and an Accurate Time Integrator
Author(s)	Saaketh Desai, Alejandro Strachan
On-Site Speaker (Planned)	Saaketh Desai
Abstract Scope	Machine learning tools are increasingly being utilized in the physical sciences to develop predictive data-based models as surrogates to physics-based approaches. These models have been extremely useful within restricted domains, resulting in scientific advances, but a lack of interpretability often limits their ability to extrapolate and satisfy physical invariants. We combine neural network training with genetic algorithms to find parsimonious models that describe the time evolution of a point particle under a highly non-linear potential. The genetic algorithm is designed to find the simplest, most interpretable network compatible with the training data and the resulting parsimonious neural networks discover Newton’s second law of motion expressed as a time integrator that conserves energy and is time reversible. By extracting underlying physics, the model significantly outperforms a generic feed-forward neural network and is immediately interpretable as the position Verlet algorithm, a non-trivial, symplectic integrator whose justification originates from Trotter’s theorem.
Proceedings Inclusion?	Planned:
Keywords	Machine Learning, Computational Materials Science & Engineering, Modeling and Simulation

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