VBFF Preprints
E. Bach, R. Baptista, J. Bröcker, B. Chen, A. Stuart; Learning Probabilistic Filters with Strictly Proper Scoring Rules
[Preprint]
H. Kaveh, R. Baptista, A. M. Stuart; Amortized Energy-Based Bayesian Inference
[Preprint]
A. Vadeboncoeur, M. Girolami, K. Bhattacharya, A. M. Stuart; Distributional Inverse Homogenization
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E. Calvello, E. Carlson, N. Kovachki, M. N. Manta, A. M. Stuart; Operator Learning for Smoothing and Forecasting
[Preprint]
K. Bhattacharya, L. Cao, A. Stuart; Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws
[Preprint]
D. Darrrow, E. Carlson, D. Goluskin; Quartic Lyapunov functions for global fluid stability, June 2026
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A. Wang, E. Carlson, F. Hoffmann; Comparing Deterministic and Stochastic Parameter Recovery Algorithms Applied to Chaotic Systems, June 2026
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E. Carlson, A. Farhat V.R. Martinez, C. Victor; Comparing Deterministic and Stochastic Parameter Recovery Algorithms Applied to Chaotic Systems, Dec 2025
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M.V. de Hoop, N. B. Kovachki, M.Lassas, and N. H. Nelsen; Extension and neural operator approximation of the electrical impedance tomography inverse map, Nov 2025
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N. H. Nelsen, H. Owhadi, A. M. Stuart, X. Yang, and Z. Zou; Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes, Oct 2025
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R. Baptista, A. M. Stuart, S. Tran; Large Language Models: A Mathematical Formulation; 2026
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H. Kaveh, R. Baptista, A. M. Stuart; Amortized Energy-Based Bayesian Inference; May 2026
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B. Yao, D. Luo, L. Cao, N. Kovachki, T. O’Leary-Roseberry, O. Ghattas; Derivative-Informed Fourier Neural Operator: Universal Approximation and Applications to PDE-Constrained Optimization, December 2025
[Preprint]
M. Raj, L. Cao, A. M. Stuart, K. Bhattacharya; A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables, May 2026
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R. Baptista, A. M. Stuart, S. Tran; A Mathematical Perspective On Contrastive Learning.
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R. Baptista, P. Birmpa, M. A. Katsoulakis, L. Rey-Bellet, B.J. Zhang; Proximal optimal transport divergences, 2025
[Preprint]
L. Conger, F. Hoffmann, R. Baptista, E. Mazumdar; Computing Optimal Transport Plans via Min-Max Gradient Flows, 2025
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R. Baptista, A. Dasgupta, N. B. Kovachki, A. Oberai, A. M. Stuart; Memorization and Regularization in Generative Diffusion Models, 2025
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F. Li, R. Baptista, Y. Marzouk; Expected information gain estimation via density approximations: Sample allocation and dimension reduction, 2024
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M. Alain, S. Takao, X. Dong, B. Rieck, E. Noutahi; Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations, 2025.
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P. Grohs, S. Lanthaler, M. Trautner; Theory-to-Practice Gap for Neural Networks and Neural Operators, 2025
[Preprint]
J. Bunker, M. Girolami, H. Lambley, A. M. Stuart, T. J. Sullivan; Autoencoders in Function Space
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S. Lanthaler, A. M. Stuart, M. Trautner; Discretization Error of Fourier Neural Operators
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E. Luk, E. Bach, R. Baptista, A.Stuart; Learning Optimal Filters Using Variational Inference
[Preprint]
R. Baptista, E. O'Reilly, Y. Xie;
TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional Regression
[Preprint]
M. Provost, R. Baptista, J. Eldredge, Y. Marzouk;
An adaptive ensemble filter for heavy-tailed distributions: tuning-free inflation and localization
[Preprint]
L. Cao, T. O'Leary-Roseberry, O. Ghattas;
Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
[Preprint]
O. Key, S. Takao, D. Giles, M. Deisenroth;
Scalable Data Assimilation with Message Passing
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E. Carlson, A. Farhat, V. Martinez, C. Victor;
Determining Modes, Synchronization, and Intertwinement
[Preprint]
E. Carlson, A. Farhat, V. Martinez, C. Victor;
On the Infinite-Nudging Limit of the Nudging Filter for Continuous Data Assimilation
[Preprint]
