A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models. - Teo Deveney; Eike Hermann Müller; Tony Shardlow (CoRR, 2019)

Deep surrogate accelerated delayed-acceptance HMC for Bayesian inference of spatio-temporal heat fluxes in rotating disc systems. - Teo Deveney; Eike Hermann Müller; Tony Shardlow (CoRR, 2022)

Closing the ODE-SDE gap in score-based diffusion models through the Fokker-Planck equation. - Teo Deveney; Jan Stanczuk; Lisa Maria Kreusser; Chris J. Budd; Carola-Bibiane Schönlieb (CoRR, 2023)

Deep Surrogate Accelerated Delayed-Acceptance Hamiltonian Monte Carlo for Bayesian Inference of Spatio-Temporal Heat Fluxes in Rotating Disc Systems. - Teo Deveney; Eike Hermann Müller; Tony Shardlow (SIAM/ASA J. Uncertain. Quantification, 2023)

Diffusion Models Encode the Intrinsic Dimension of Data Manifolds. - Jan Stanczuk; Georgios Batzolis; Teo Deveney; Carola-Bibiane Schönlieb (ICML, 2024)

Equidistribution-based training of Free Knot Splines and ReLU Neural Networks. - Simone Appella; Simon R. Arridge; Chris J. Budd; Teo Deveney; Lisa Maria Kreusser (CoRR, 2024)

G-Adaptive mesh refinement - leveraging graph neural networks and differentiable finite element solvers. - James Rowbottom; Georg Maierhofer; Teo Deveney; Katharina Schratz; Pietro Liò; Carola-Bibiane Schönlieb; Chris J. Budd (CoRR, 2024)