Physical Foundation Models

Follow this topic Ask about this topic

Latest papers

14 papers4 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

From Simulated Fluids to Living Tissue: Zero-Shot Foundation Model Transfer to Perturbed Embryos
Oct 12, 2026Louisa Cornelis, Haewon Jeong, Irina Espejo +1Physical Foundation Models Transfer Learning
UC Santa Barbara · PolymathicAI · New York University +1
Physics Foundation Models for Efficient Control of Fluid Flows
Oct 10, 2026Mahindra Rautela, Shaifalee Saxena, Alexander ScheinkerPhysical Foundation Models Policy Learning
Los Alamos National Laboratory, Los Alamos, NM, USA · University of New Mexico, Albuquerque, NM, USA
Mesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data
Oct 8, 2026Georg Maierhofer, Miles Cranmer, Payel MukhopadhyayPhysical Foundation Models Convolutional Neural Networks
University of Cambridge
A foundation model for energy and radiation systems built on heterogeneous scientific interfaces
Sep 29, 2026Samrendra Roy, Tapas Tripura, Yoon Pyo Lee +2Physical Foundation Models PDE Surrogate Modeling
Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA · Department of Applied Mechanics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India · Yardi School of Artificial Intelligence (ScAI), Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India +1
Human-Level Accuracy, Non-Human Strategies: Revealing Model-Human Divergence in Video Physical Reasoning
Sep 19, 2026Fanhong Li, Shurui Zheng, Zi Yin +3Physical Reasoning in Video Physical Foundation Models
Tsinghua University, Beijing, China · ModelBest Inc., Beijing, China · Microsoft, Beijing, China
PhysBrain 1.5: From Vision-Language Models to Physical Foundation Models
Sep 14, 2026DeepCybo Team, Yu Bin, Haipeng Cao +51Robot Motion Generation Physical Foundation Models
Sparse probes and murky physics: a case study of interpretability challenges in a foundation model for continuum dynamics
Jun 10, 2026Katherine Rosenfeld, Maike SonnewaldNeural Surrogate Modeling Mechanistic Interpretability
Gates Foundation · UC Davis
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
May 31, 2026Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux +20Sim-to-Real Transfer Physical Foundation Models
University of Cambridge, UK · Polymathic AI · CEA, DAM/DIF, 91297 Arpajon Cedex, France +6
Do Physics Foundation Models Learn Generalizable Physics? A Bias-Aware Benchmark Across Physical Regimes and Distribution Shifts
May 28, 2026Mengdi Chu, Yang Liu, Ayan Biswas +1Distribution Shift Robustness Neural Network Generalization
The Ohio State University · Los Alamos National Laboratory
SAFE-SVD: Sensitivity-Aware Fidelity-Enforcing SVD for Physics Foundation Models
May 18, 2026Chengjie Hong, Feixiang He, Yiheng Zeng +2Model Compression Physical Foundation Models
University College London, UK · Central South University, China · University of Massachusetts at Amherst, US +1
Physical Foundation Models: Fixed hardware implementations of large-scale neural networks
Apr 30, 2026Logan G Wright, Tianyu Wang, Tatsuhiro Onodera +1Efficient Neural Network Inference AI Accelerator Inference
Department of Applied Physics, Yale University, New Haven, CT 06520, USA · Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA · School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA +2
On the Value of Tokeniser Pretraining in Physics Foundation Models
Mar 5, 2026Hadi Sotoudeh, Payel Mukhopadhyay, Ruben Ohana +4Physical Foundation Models Physics-Based Simulation
University of Cambridge · Flatiron Institute · New York University +1
LUMINA: Foundation Models for Topology Transferable ACOPF
Mar 4, 2026Yijiang Li, Zeeshan Memon, Hongwei Jin +7Physical Foundation Models Scientific ML
Argonne National Laboratory, Lemont, IL 60439, USA · Emory University, Atlanta, GA 30322, USA · Sogang University, Seoul, South Korea
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Nov 19, 2025Michael McCabe, Payel Mukhopadhyay, Tanya Marwah +22Physics-Based Simulation Computational Fluid Dynamics
Polymathic AI · Flatiron Institute · New York University +9

Oct 12, 2026J/K move · Enter open · S save

From Simulated Fluids to Living Tissue: Zero-Shot Foundation Model Transfer to Perturbed Embryos

Louisa Cornelis, Haewon Jeong, Irina Espejo, Payel Mukhopadhyay

UC Santa Barbara · PolymathicAI · New York University+1

Physical Foundation Models Transfer Learning Zero-Shot Learning

Open paper AskPDF arXiv

Morphogenesis is driven by the interplay of genetic regulation and tissue mechanics. In vivo video data is limited, particularly under genetic mutations and environmental perturbations, making generalization beyond the most representative conditions in the dataset challenging. However, at tissue scales, the dynamics of Drosophila can be described as a continuum flow, motivating the usage of representations learned across other physical systems as priors for developmental dynamics. We investigate finetuning Walrus, a Continuum Dynamics Foundation Model pretrained across a broad set of simulated continuous systems, on tissue velocity fields from only 12 wild-type Drosophila blastoderm videos. We evaluate autoregressive prediction both in-distribution and zero-shot under unseen temperature and genetic perturbations. Zero-shot refers to evaluation on perturbation conditions never seen during wild-type finetuning, without any additional training. In-distribution, a Fourier Neural Operator model trained from scratch achieves best performance on VRMSE. However, Walrus (CRPS) is the strongest model under the most severe distribution shift of unseen samples of mutated embryos and temperature conditions. Interestingly, predictions capture the shape of the mean surface tissue velocity trajectory and its separation between 17°C and 27°C, including the timing of the peak at Germ Band Extension onset, a biologically meaningful milestone of embryo development. Our results provide preliminary support that representations learned from different dynamical systems can transfer to experimental morphogenesis in developmental regimes, provided they admit a continuum description. More broadly, our results motivate further study of physics foundation models as a new complementary tool for the simulation of morphogenesis in low-data regimes.