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Data Driven Closure Relation
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July 2026 Publications List

Published

  1. S. Andren, B. S. Kang, W. Goddard, J. Eiler, and A. Anandkumar. "Accelerated Isotopologue Reduced Partition Function Ratio Prediction with Orbital-based Deep Learning." In NeurIPS 2025 AI for Science Workshop (2025).
  2. E. Bach, T. Colonius, I. Scherl, and A. Stuart, "Filtering dynamical systems using observations of statistics," Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 34, no. 3, 2024.
  3. J. Berner, M. Liu-Schiaffini, J. Kossaifi, V. Duruisseaux, B. Bonev, K. Azizzadenesheli, A. Anandkumar. Principled approaches for extending neural architectures to function spaces for operator learning. Nat Mach Intell (2026).
  4. K. Bhattacharya, L. Cao, A. M. Stuart; Optimal Experimental Design for Reliable Learning of history-dependent Constitutive Laws, Computer Methods in Applied Mechanics and Engineering, Vol. 457, 2026.
  5. K. Bhattacharya, Multiscale modeling of materials and neural operators, MRS Bulletin, DOI: 10.1557/s43577-026-01117-8, 2026
  6. S. Catsoulis, G. Stepaniants, A. Stuart, T. Colonius. Neural operator-enabled closure for stochastically forced Burgers' equation. 78th Annual Meeting of the Division of Fluid Dynamics, 2025
  7. C. Christopoulos, I. Lopez-Gomez, T. Beucler, Y. Cohen, C. Kawczynski, O.R.A Dunbar, T. Schneider, 2024: Online learning of entrainment closures in a hybrid machine learning parameterization. Journal of Advances in Modeling Earth Systems, 16, e2024MS004485.
  8. R. Deshpande, F. Mottes, A.-D. Vlad, M. P. Brenner, and A. Dal Co, "Engineering morphogenesis of cell clusters with differentiable programming," Nature Computational Science, vol. 5, no. 10, pp. 875–883, 2025.
  9. O.R.A. Dunbar, C.M Elliott, L.M., Kreusser, 2025: Models for information propagation on graphs. European Journal of Applied Mathematics, 2025, 1-22.
  10. O.R.A. Dunbar, N.H. Nelsen, M. Mutic, 2025: Hyperparameter optimization for randomized algorithms: A case study on random features. Statistics and Computing, 35, 56.
  11. V. Duruisseaux, J. Kossaifi, A. Anandkumar., "Fourier Neural Operators Explained: A Practical Perspective". arXiv 2025.
  12. R. J. George, C. Eisenach, U. Ghai, D. Perrault-Joncas, A. Anandkumar, and D. Foster. "BRIDGE: Building Representations in Domain-Guided Verified Program Synthesis." In ICML 2026 AI for Math Workshop, 2026.
  13. R. J. George, S. Huang, P. Song, and A. Anandkumar. "LeanProgress: Guiding Search for Neural Theorem Proving via Proof Progress Prediction." Transactions on Machine Learning Research, 2025.
  14. L. Ghafourpour, V. Duruisseaux, B. Tolooshams, P. H. Wong, C. A. Anastassiou, A. Anandkumar, "NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models", NeurIPS 2025.
  15. R. Hsiang, W. Adkisson, R. J. George, and A. Anandkumar. "LeanDojo-v2: A Comprehensive Library for AI-Assisted Theorem Proving in Lean." In NeurIPS 2025 Workshop on Mathematical Reasoning and AI, 2025.
  16. A. S. Jatyani, J. Wang, R. Y. Lin, V. Duruisseaux, A. Anandkumar, "Coarse-to-Fine 3D MRI Reconstruction via 3D Neural Operators", NeurIPS Workshop on Imageomics, 2025.
  17. X. Ju, J. Yao, A. Anandkumar, S. M. Benson, and G. Wen. "Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage." In AI {\&} PDE: ICLR 2026 Workshop on AI and Partial Differential Equations.
  18. R. K. Krueger, M. C. Engel, R. Hausen, and M. P. Brenner, "Fitting coarse-grained models to macroscopic experimental data via automatic differentiation," Proceedings of the National Academy of Sciences, vol. 123, no. 14, p. e2508255123, 2026.
  19. R. K. Krueger, M. P. Brenner, and K. Shrinivas, "Generalized design of sequence–ensemble–function relationships for intrinsically disordered proteins," Nature Computational Science, vol. 6, no. 5, pp. 512–523, 2026.
  20. X. Li, Z. Li, N. Kovachki, and A. Anandkumar., "Geometric Operator Learning with Optimal Transport," Journal of Machine Learning Research 26, no. 25-1380 (2025)
  21. T. Y. L. Lin, J. Yao, L. Chiang, J. Berner, and A. Anandkumar. "Decoupled Diffusion Solver for Inverse Problems on Function Spaces." In AI {\&} PDE: ICLR 2026 Workshop on AI and Partial Differential Equations.
  22. R. Y. Lin, J. Berner, V. Duruisseaux, D. Pitt, D. Leibovici, J. Kossaifi, K. Azizzadenesheli, and A. Anandkumar, "Enabling Automatic Differentiation with Mollified Graph Neural Operators", TMLR 2025.
  23. S. Liu, N. Lapusta and K. Bhattacharya, "Learning a potential formulation for rate-and-state friction", Mechanics of Materials, 212: 105540, 2025
  24. D. Nguyen, J. Wang, J.Yao, J. Berner, and A. Anandkumar. "Flow-Guided Neural Operator for Self-Supervised Learning on Time Series Data." In NeurIPS 2025 Workshop on Learning from Time Series for Health
  25. A. Pipi, N. Gopinath, V. Duruisseaux, M. G. Marmarelis, T. L. Patti, B. Khailany, P. Narang, A. Anandkumar, "Inverse Design with Fourier Neural Operators for Quantum System Control", NeurIPS Workshop on Machine Learning and the Physical Sciences, 2025.
  26. M. Raj, L. Cao, A. Stuart, K. Bhattacharya; A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables.
  27. H. Singh, M. Raj, and K. Bhattacharya, "Effective behavior of heterogeneous media governed by strain gradient elasticity," Journal of the Mechanics and Physics of Solids, 212: 106583, 202
  28. A. Vadeboncoeur, M. Girolami, K. Bhattacharya, A. M. Stuart; Distributional Inverse Homogenization
  29. S. Vernon, E. Bach, O.R.A. Dunbar, 2025: Nesterov acceleration for ensemble Kalman inversion and variants. Journal of Computational Physics, 535, 114063.
  30. J. Wu, R. J. George, and A. Anandkumar. "ITPEval: Benchmarking Formal Translation Across Interactive Theorem Provers." In ICML 2026 AI for Math Workshop, 2026.
  31. J. Yao, A. Mammadov, J. Berner, G. Kerrigan, J. Chul Ye, K. Azizzadenesheli, and A. Anandkumar. "Guided diffusion sampling on function spaces with applications to pdes." Advances in Neural Information Processing Systems 38 (2026): 127057-127094.
  32. C. Zhang, A. Zhao, R. J. George, S.Gukov, and A. Anandkumar. "Mathematical Discovery and Formalization Towards the AC Conjecture." In NeurIPS 2025 Workshop on Mathematical Reasoning and AI, 2025.

Preprints (including those in review but excluding those that are published)

  1. E. Bach, R. Baptista, D. Sanz-Alonso, and A. Stuart, "Machine Learning for Inverse Problems and Data Assimilation," arXiv preprint arXiv:2410.10523, 2024.
  2. K. Bhattacharya, L. Cao, G. Stepaniants, A. Stuart, and M. Trautner, "Learning memory and material dependent constitutive laws," SMAI J Comp. Math. accepted.
  3. R. Gjini, M. Morzfeld, O. R. A. Dunbar, and T. Schneider, "The ensemble Kalman inversion race," Journal of Advances in Modeling Earth Systems, submitted, 2025.
  4. G. R. Joseph, J. Cruden, W. Adkisson, X. Zhong, H. Zhang, and A. Anandkumar. "TorchLean: Formalizing Neural Networks in Lean." arXiv preprint arXiv:2602.22631, 2026.
  5. H. Kaveh, O. R. A. Dunbar, J. P. Avouac, and A. M. Stuart, "Bayesian calibration of dynamic models of earthquake sequences using observations from past large earthquakes," Journal of Advances in Modeling Earth Systems, submitted, 2026.
  6. B. S. Kang, V. C. Bhethanabotla, A. Tavakoli, M. D. Hanisch, W. A. Goddard III, and A. Anandkumar. "OrbitAll: a unified quantum mechanical representation deep learning framework for all molecular systems." arXiv preprint arXiv:2507.03853 (2025).
  7. F. Mottes, Q.-Z. Zhu, and M. P. Brenner, "Gradient-based optimization of exact stochastic kinetic models," arXiv preprint arXiv:2601.14183, 2026.
  8. J. V. Roggeveen and M. P. Brenner, "Meshless solutions of PDE inverse problems on irregular geometries," arXiv preprint arXiv:2510.25752, 2025.
  9. A. M. Sunol, J. V. Roggeveen, M. G. Alhashim, H. S. Bae, and M. P. Brenner, "Learning constitutive models and rheology from partial flow measurements," arXiv preprint arXiv:2510.24673, 2025.