One-Shot Transfer Learning of Physics-Informed Neural Networks

1 code implementation • 21 Oct 2021 • Shaan Desai, Marios Mattheakis, Hayden Joy, Pavlos Protopapas, Stephen Roberts

In this study, we present a general framework for transfer learning PINNs that results in one-shot inference for linear systems of both ordinary and partial differential equations.

Transfer Learning

Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems

1 code implementation • 16 Jul 2021 • Shaan Desai, Marios Mattheakis, David Sondak, Pavlos Protopapas, Stephen Roberts

In this study, we address the challenge of learning from such non-autonomous systems by embedding the port-Hamiltonian formalism into neural networks, a versatile framework that can capture energy dissipation and time-dependent control forces.

Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL

no code implementations • NeurIPS 2021 • Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen Roberts

Despite a series of recent successes in reinforcement learning (RL), many RL algorithms remain sensitive to hyperparameters.

Data Augmentation Hyperparameter Optimization +1

Variational Integrator Graph Networks for Learning Energy Conserving Dynamical Systems

1 code implementation • 28 Apr 2020 • Shaan Desai, Marios Mattheakis, Stephen Roberts

Using this framework we introduce Variational Integrator Graph Networks - a novel method that unifies the strengths of existing approaches by combining an energy constraint, high-order symplectic variational integrators, and graph neural networks.

Inductive Bias

DYNOTEARS: Structure Learning from Time-Series Data

4 code implementations • 2 Feb 2020 • Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Paul Beaumont, Konstantinos Georgatzis, Bryon Aragam

Compared to state-of-the-art methods for learning dynamic Bayesian networks, our method is both scalable and accurate on real data.

Time Series Time Series Analysis