6 papers
Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm
Shizheng Wen, Mingyuan Chi, Tianwei Yu +5
We present a unified algorithmic framework for the numerical solution, constrained optimization, and physics-informed learning of PDEs with a variational structure. Our framework i…
ELM-FBPINNs: An Efficient Multilevel Random Feature Method
Samuel Anderson, Victorita Dolean, Ben Moseley +1
Domain-decomposed variants of physics-informed neural networks (PINNs) such as finite basis PINNs (FBPINNs) mitigate some of PINNs' issues like slow convergence and spectral bias t…
Extending quantum theory with AI-assisted deterministic game theory
Florian Pauschitz, Ben Moseley, Ghislain Fourny
We present an AI-assisted framework for predicting individual runs of complex quantum experiments, including contextuality and causality (adaptive measurements), within our long-te…
PTL-PINNs: Perturbation-Guided Transfer Learning with Physics- Informed Neural Networks for Nonlinear Systems
Duarte Alexandrino, Ben Moseley, Pavlos Protopapas
Accurately and efficiently solving nonlinear differential equations is crucial for modeling dynamic behavior across science and engineering. Physics-Informed Neural Networks (PINNs…
Local Feature Filtering for Scalable and Well-Conditioned Domain-Decomposed Random Feature Methods
Jan Willem van Beek, Victorita Dolean, Ben Moseley
Random Feature Methods (RFMs) and their variants such as extreme learning machine finite-basis physics-informed neural networks (ELM-FBPINNs) offer a scalable approach for solving…
Modern, Efficient, and Differentiable Transport Equation Models using JAX: Applications to Population Balance Equations
Mohammed Alsubeihi, Arthur Jessop, Ben Moseley +2
Population balance equation (PBE) models have potential to automate many engineering processes with far-reaching implications. In the pharmaceutical sector, crystallization model-b…