arXiv:2606. 28546v1 Announce Type: new Abstract: Recent advances in AI-driven weather and climate modeling have improved forecast skill while reducing computational cost.
Inertial confinement fusion (ICF) is a leading pathway toward clean energy, but each shot at the National Ignition Facility costs on the order of one million dollars, making accurate AI surrogates a h...
arXiv:2609.26095v1 Announce Type: cross Abstract: With the growing global demand for energy, nuclear fusion has emerged as a promising direction for future clean energy. Tokamaks represent one of the...
arXiv:2608. 02769v1 Announce Type: cross Abstract: Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance.
IGNITE is a generative world foundation model designed to simulate fusion plasma behavior in the DIII‑D National Fusion Facility. Trained in a self‑supervised way on more than a decade of unlabeled experimental data, its core dynamics model can predict entire discharges from specified actuator trajectories, which may be supplied or generated from textual prompts or desired outcomes. The architecture uses spatio‑temporal tokenizers for diverse input modalities—time‑series measurements, image sequences, and high‑resolution spectrograms—feeding an auto‑regressive backbone that can forecast discharges over an infinite horizon.
arXiv:2608. 07183v1 Announce Type: new Abstract: Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost constraints routinely produce incomplete observations.
arXiv:2609.07756v1 Announce Type: new Abstract: Inertial confinement fusion (ICF) is a leading pathway toward clean energy, but each shot at the National Ignition Facility costs on the order of one m...
arXiv:2605. 17985v2 Announce Type: replace-cross Abstract: We propose a new method for compressing physics foundation models (PFMs) which is a new trend in AI for Science.
Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this challenge, ultra-peripheral collisions (UPCs) provide a femtoscopic tomography for imaging the atomic nucleus.
arXiv:2607. 05252v1 Announce Type: new Abstract: Simulation-Based Inference (SBI) is critical for scientific discovery, with generative models offering a promising path toward efficient inference.
arXiv:2605. 29283v2 Announce Type: replace-cross Abstract: Recent physics foundation models claim general spatiotemporal forecasting ability, yet their evaluations often collapse performance into a single average score under a fixed training distribution.
The paper introduces PhyMo, a physics‑grounded multimodal framework that uses a physical‑field modality to represent heterogeneous measurements via PDE‑associated operators. It follows a three‑stage learning process: pretraining a physical‑field encoder with PDE residual supervision, aligning its representations with visual embeddings in a shared latent space, and applying downstream prediction heads to the fused multimodal representations. Experiments on five diverse physical datasets show that PhyMo outperforms the strongest baseline on each dataset, establishing its effectiveness for multimodal representation learning in AI for Physics.