Assistant Research Professor, Physics-AI fellow
Publications
For an up-to-date publication list and citation metrics, see Google Scholar: https://scholar.google.com/citations?user=KtPHj74AAAAJ&hl=en
AI, Foundation models, Machine Learning, Scientific Computing
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence. P. Mukhopadhyay, S. S. Nixon, R. Watteaux et al. arXiv:2606.01470, 2026. https://arxiv.org/abs/2606.01470
Probabilistic Retrofitting of Learned Simulators. C. Diaconu, M. Cranmer, R. E. Turner, T. Marwah, and P. Mukhopadhyay. ICML 2026. https://arxiv.org/abs/2603.01949
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics. M. McCabe, P. Mukhopadhyay, T. Marwah et al. ICML 2026 Spotlight. https://arxiv.org/abs/2511.15684
On the Value of Tokeniser Pretraining in Physics Foundation Models. H. Sotoudeh, P. Mukhopadhyay, R. Ohana, M. McCabe, N. D. Lawrence, S. Ho, and M. Cranmer. ICLR 2026 AI&PDE Workshop. https://arxiv.org/abs/2603.05598
Overtone: Cyclic Patch Modulation for Clean, Efficient, and Flexible Physics Emulators. P. Mukhopadhyay, M. McCabe, R. Ohana, and M. Cranmer. ICLR 2026. https://arxiv.org/abs/2507.09264
Physics Steering: Causal Control of Cross-Domain Concepts in a Physics Foundation Model. R. A. Fear, P. Mukhopadhyay, M. McCabe, A. Bietti, and M. Cranmer. NeurIPS 2025 Workshop. https://arxiv.org/abs/2511.20798
Predicting Partially Observable Dynamical Systems via Diffusion Models with a Multiscale Inference Scheme. R. Morel, F. P. Ramunno, J. Shen, A. Bietti, K. Cho, M. Cranmer, S. Golkar, O. Gugnin, G. Krawezik, T. Marwah, M. McCabe, L. Meyer, P. Mukhopadhyay et al. NeurIPS 2025. https://arxiv.org/abs/2511.19390
AION-1: Omnimodal Foundation Model for Astronomical Sciences. L. Parker, F. Lanusse, J. Shen, O. Liu, T. Hehir, L. Sarra, L. Meyer, M. Bowles, S. Wagner-Carena, H. Qu, S. Golkar, A. Bietti, H. Bourfoune, N. Casserau, P. Cornette, K. Hirashima, G. Krawezik, R. Ohana, N. Lourie, M. McCabe, R. Morel, P. Mukhopadhyay et al. NeurIPS 2025. https://arxiv.org/abs/2510.17960
Compute-Adaptive Surrogate Modeling of Partial Differential Equations. P. Mukhopadhyay, M. McCabe, R. Ohana, and M. Cranmer. ICLR 2025 Workshop on Machine Learning for Multi-Phase Phenomena. https://openreview.net/forum?id=YM3koX4nHp
The Well: A Large-Scale Collection of Diverse Physics Simulations for Machine Learning. R. Ohana, M. McCabe, et al., NeurIPS 2024. https://arxiv.org/abs/2412.00568
Astrophysics and Particle Physics
Angle-Dependent In Situ Fast Flavor Transformations in Post-Neutron-Star-Merger Disks. K. A. Lund, P. Mukhopadhyay, J. M. Miller, and G. C. McLaughlin. Astrophysical Journal Letters, 2025. https://arxiv.org/abs/2503.23727
Successful νp-process in Neutrino-Driven Outflows in Core-Collapse Supernovae. A. Friedland, P. Mukhopadhyay, and A. V. Patwardhan. Journal of Cosmology and Astroparticle Physics, 2025. https://arxiv.org/abs/2312.03208
The Time Evolution of Fast Flavor Crossings in Post-Merger Disks Around a Black Hole Remnant. P. Mukhopadhyay, J. M. Miller, and G. C. McLaughlin. Astrophysical Journal, 2024. https://arxiv.org/abs/2404.17938
Reacceleration of Galactic Cosmic Rays Beyond the Knee at the Termination Shock of a Cosmic-Ray-Driven Galactic Wind. P. Mukhopadhyay, E. Peretti, N. Globus, P. Simeon, and R. Blandford. Astrophysical Journal, 2023. https://arxiv.org/abs/2301.08902
Near-Critical Supernova Outflows and Their Neutrino Signatures. A. Friedland and P. Mukhopadhyay. Physics Letters B, 2022. https://arxiv.org/abs/2009.10059
Self-Generated Cosmic-Ray Turbulence Can Explain the Morphology of TeV Halos. P. Mukhopadhyay and T. Linden. Physical Review D, 2022. https://arxiv.org/abs/2111.01143
Celestial-Body Focused Dark Matter Annihilation Throughout the Galaxy. R. K. Leane, T. Linden, P. Mukhopadhyay, and N. Toro. Physical Review D, 2021. https://arxiv.org/abs/2101.12213
Carter Constant and Superintegrability. P. Mukhopadhyay and R. K. Nayak. International Journal of Modern Physics D, 2018. https://arxiv.org/abs/1804.08169
Quark Stars Admixed with Dark Matter. P. Mukhopadhyay and J. Schaffner-Bielich. Physical Review D, 2016. https://arxiv.org/abs/1511.00238