A JAX-based differentiable model of pressure vacuum swing adsorption accelerates cyclic steady-state simulation by 20x via Newton iteration and produces a better Pareto front with IPOPT than NSGA-II in two orders of magnitude less time on a post-combustion capture benchmark.
A machine learning framework for data driven acceleration of computations of differential equations
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abstract
We propose a machine learning framework to accelerate numerical computations of time-dependent ODEs and PDEs. Our method is based on recasting (generalizations of) existing numerical methods as artificial neural networks, with a set of trainable parameters. These parameters are determined in an offline training process by (approximately) minimizing suitable (possibly non-convex) loss functions by (stochastic) gradient descent methods. The proposed algorithm is designed to be always consistent with the underlying differential equation. Numerical experiments involving both linear and non-linear ODE and PDE model problems demonstrate a significant gain in computational efficiency over standard numerical methods.
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The LC-prior GP combines POD-reduced coefficients with a physics-corrected prior and RBF-FD data generation to surrogate nonlinear multi-coupled PDEs on irregular 2D domains more efficiently than standard approaches.
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Accelerating Simulation and Optimisation of Cyclic Adsorption Processes with Differentiable Programming
A JAX-based differentiable model of pressure vacuum swing adsorption accelerates cyclic steady-state simulation by 20x via Newton iteration and produces a better Pareto front with IPOPT than NSGA-II in two orders of magnitude less time on a post-combustion capture benchmark.
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Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry
The LC-prior GP combines POD-reduced coefficients with a physics-corrected prior and RBF-FD data generation to surrogate nonlinear multi-coupled PDEs on irregular 2D domains more efficiently than standard approaches.