Mr David Dalton
- Research Associate (Statistics)
Publications
2026
Dalton, David, Gao, Hao 
ORCID: https://orcid.org/0000-0001-6852-9435 and Husmeier, Dirk ORCID: https://orcid.org/0000-0003-1673-7413
(2026)
FlowPINNs: a Variational Framework for PDE Parameter Inference
and Uncertainty Quantification.
In: Twenty-Ninth Annual Conference on Artificial Intelligence and Statistics (AISTATS 2026), Tangier, Morocco, 02-05 May 2026,
(Accepted for Publication)
Dubey, Vijay K., Haese, Collin E., Gültekin, Osman, Dalton, David, Rausch, Manuel K. and Fuhg, Jan (2026) Graph neural network surrogates for contacting deformable bodies with necessary and sufficient contact detection. Computer Methods in Applied Mechanics and Engineering, 448, 118413. (doi: 10.1016/j.cma.2025.118413)
2024
Dalton, David, Lazarus, Alan, Gao, Hao ORCID: https://orcid.org/0000-0001-6852-9435 and Husmeier, Dirk ORCID: https://orcid.org/0000-0003-1673-7413
(2024)
Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations.
Journal of Machine Learning Research, 25(272),
pp. 1-61.
Dalton, David, Husmeier, Dirk ORCID: https://orcid.org/0000-0003-1673-7413 and Gao, Hao ORCID: https://orcid.org/0000-0001-6852-9435
(2024)
Physics and Lie Symmetry Informed Gaussian Processes.
In: 41st International Conference on Machine Learning, Vienna, Austria, 21-27 Jul 2024,
pp. 9953-9975.
Articles
Dubey, Vijay K., Haese, Collin E., Gültekin, Osman, Dalton, David, Rausch, Manuel K. and Fuhg, Jan (2026) Graph neural network surrogates for contacting deformable bodies with necessary and sufficient contact detection. Computer Methods in Applied Mechanics and Engineering, 448, 118413. (doi: 10.1016/j.cma.2025.118413)
Dalton, David, Lazarus, Alan, Gao, Hao ORCID: https://orcid.org/0000-0001-6852-9435 and Husmeier, Dirk ORCID: https://orcid.org/0000-0003-1673-7413
(2024)
Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations.
Journal of Machine Learning Research, 25(272),
pp. 1-61.
Conference Proceedings
Dalton, David, Gao, Hao ORCID: https://orcid.org/0000-0001-6852-9435 and Husmeier, Dirk ORCID: https://orcid.org/0000-0003-1673-7413
(2026)
FlowPINNs: a Variational Framework for PDE Parameter Inference
and Uncertainty Quantification.
In: Twenty-Ninth Annual Conference on Artificial Intelligence and Statistics (AISTATS 2026), Tangier, Morocco, 02-05 May 2026,
(Accepted for Publication)
Dalton, David, Husmeier, Dirk ORCID: https://orcid.org/0000-0003-1673-7413 and Gao, Hao ORCID: https://orcid.org/0000-0001-6852-9435
(2024)
Physics and Lie Symmetry Informed Gaussian Processes.
In: 41st International Conference on Machine Learning, Vienna, Austria, 21-27 Jul 2024,
pp. 9953-9975.