Arnaud Vadeboncoeur

11 papers A* 1Journal 10

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Hierarchical Inference and Closure Learning via Adaptive Surrogates for ODEs and PDEs. CoRR Pengyu Zhang, Arnaud Vadeboncoeur, Alex Glyn-Davies, Mark Girolami
2025	J	`jnl`	Efficient Deconvolution in Populational Inverse Problems. CoRR Arnaud Vadeboncoeur, Mark Girolami, Andrew M. Stuart
2025	J	`jnl`	Efficient Prior Calibration from Indirect Data. SIAM J. Sci. Comput. Ömer Deniz Akyildiz, Mark Girolami, Andrew M. Stuart, Arnaud Vadeboncoeur
2025	J	`jnl`	Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later. CoRR Arnaud Vadeboncoeur, Gregory Duthé, Mark Girolami, Eleni N. Chatzi
2025	J	`jnl`	Probabilistic Super-Resolution for High-Fidelity Physical System Simulations with Uncertainty Quantification. CoRR Pengyu Zhang, Connor Duffin, Alex Glyn-Davies, Arnaud Vadeboncoeur, Mark Girolami
2024	J	`jnl`	A Primer on Variational Inference for Physics-Informed Deep Generative Modelling. CoRR Alex Glyn-Davies, Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami
2024	J	`jnl`	Efficient Prior Calibration From Indirect Data. CoRR Ömer Deniz Akyildiz, Mark Girolami, Andrew M. Stuart, Arnaud Vadeboncoeur
2023	J	`jnl`	Fully probabilistic deep models for forward and inverse problems in parametric PDEs. J. Comput. Phys. Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak
2023	A*	`conf`	Random Grid Neural Processes for Parametric Partial Differential Equations. ICML Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz
2023	J	`jnl`	Random Grid Neural Processes for Parametric Partial Differential Equations. CoRR Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz
2022	J	`jnl`	Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs. CoRR Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak