363 citations · 462 across the 10 of their papers we have counts for
4 papers · 1 filter
Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Data
Arthur Jessop, Mohammed Alsubeihi, Ben Moseley +1
Transport models underpin the design and optimisation of chemical processes, yet their constitutive laws are typically hand-picked, their experimental initial conditions are assume…
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…
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…
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…