Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction

no code implementations • 23 Jun 2020 • Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider

We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models in various gray-box settings which incorporates prior knowledge in the form of systems of ordinary differential equations.

Neural Dynamical Systems

no code implementations • ICLR Workshop DeepDiffEq 2019 • Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider

We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models which incorporates prior knowledge in the form of systems of ordinary differential equations.

Offline Contextual Bayesian Optimization for Nuclear Fusion

no code implementations • 6 Jan 2020 • Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider

One obstacle in utilizing fusion as a feasible energy source is the stability of the reaction.

Bayesian Optimization

Offline Contextual Bayesian Optimization

1 code implementation • NeurIPS 2019 • Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark Boyer, Egemen Kolemen

In black-box optimization, an agent repeatedly chooses a configuration to test, so as to find an optimal configuration.

Bayesian Optimization