⚛️ Physics & Scientific Computing

🧠 NeurIPS2026 · 5 paper notes

📌 Same area in other venues: 🎞️ ECCV2026 (5) · 📷 CVPR2026 (2) · 🔬 ICLR2026 (69) · 🧪 ICML2026 (33) · 🤖 AAAI2026 (15) · 🧠 NeurIPS2025 (57)

Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events

The paper compresses a reference-nudged stochastic coarse ensemble into dynamical-sensitivity context and uses FiLM to modulate existing temporal correctors, improving QG extreme-event area and frequency statistics with limited high-resolution training data without guaranteeing better global distributions or every tail diagnostic than a long-data baseline.

Neural Harmonic Measure Operator

NHMO learns a geometry-only harmonic-measure density, integrates boundary data against its normalized kernel, and uses a learned lift for source contributions and approximation error, outperforming four compared baselines across all five 3D MCB-B Poisson categories while reducing repeated solves on one geometry to a cached matrix-vector product and a lift forward pass.

Phaedra: Learning High-Fidelity Discrete Tokenization for the Physical Sciences

Phaedra separates physical-field latents into multidimensional morphology tokens and high-precision one-dimensional amplitude tokens, then learns to recombine and decode them, reducing in-distribution PDE reconstruction nMAE from FSQ's 2.603 to 1.522 and compressing 65.0 GB of scientific data to 3.44 GB.

PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation

PR-Smoother learns a non-Gaussian initial-state posterior and future-conditioned stepwise corrections in physical space while retaining the prescribed simulator, using an observation-only variational objective to jointly estimate states, physical parameters, and sensor biases, with validation on nonlinear Lorenz–96 and a 16,384-dimensional fluid system.

Soft Geometric Inductive Bias for Object Centric Dynamics

The paper encodes provided object states as Clifford multivectors and learns next-timestep states with geometric-product layers and a block-causal Transformer that do not enforce exact equivariance, improving prediction and autoregressive rollouts in symmetry-breaking settings such as wall collisions, anisotropic confinement, and real driving trajectories.