⚛️ Physics & Scientific Computing¶

📷 CVPR2026 · 10 paper notes

📌 Same area in other venues: 🧠 NeurIPS2026 (16) · 🎞️ ECCV2026 (9) · 🔬 ICLR2026 (76) · 🧪 ICML2026 (36) · 🤖 AAAI2026 (16) · 🧠 NeurIPS2025 (64)

🔥 Top topics: Diffusion Models ×2

AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts: AviaSafe embeds the "localization before quantification" hierarchical strategy and the long-validated "Icing Condition (IC) index" into a Swin Transformer backbone. It achieves the first global, 6-hourly, phase-separable (ice/liquid/rain/snow) cloud microphysics forecast, outperforming the FuXi baseline on 93.7% of variable-lead time combinations and matching or exceeding the operational NWP ECMWF HRES on key background variables up to a 7-day lead time.
Spatial-Spectral Residuals Informed Diffusion Neural Operator for Pan-sharpening: SRINO replaces the attention-based denoising backbone of diffusion models for pan-sharpening with a Galerkin-type Neural Operator (transferring the generation process to a continuous function space to significantly save FLOPs and memory). It treats pixel-level spatial/spectral consistency residuals directly as conditions fed into each step of the reverse sampling process for closed-loop guidance. On WV3/GF2/QB datasets, it outperforms current SOTA methods while being several times more computationally efficient than attention-based diffusion models.
Computational Speckle Pattern Interferometry: Reformulates classic Electronic Speckle Pattern Interferometry (ESPI) into an inner product model of "speckle appearance vector \(\times\) displacement phasor vector". It calibrates the system without precise phase-shifting hardware via a single matrix factorization, and then recovers sub-micron per-pixel displacements from a single speckle pattern using a Horn-Schunck-style energy functional, while additionally reading out the amount of in-exposure motion using the phasor amplitude.
Electromagnetic Inverse Scattering from a Single Transmitter: This paper reformulates the Electromagnetic Inverse Scattering Problem (EISP) from "per-sample physical optimization" to "end-to-end data-driven regression." By using an MLP to directly map received scattered fields and spatial coordinates to local relative permittivity, the method leverages data distribution priors learned from the training set to compensate for the information deficiency in sparse measurements. It achieves high-quality reconstruction using only a single transmitter for the first time, with inference speeds over 70,000 times faster than previous SOTA methods.
FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes: The Reproducing Kernel Particle Method (RKPM) is utilized to parameterize the skinning weights of elastic bodies. Optimal skinning eigenmodes are then directly solved via a generalized eigenvalue problem on the elastic energy Hessian, enabling meshless, reduced-order elasticity simulation. This approach is approximately 40× faster to train than Simplicits (which uses per-object neural field optimization) while achieving accuracy closer to the FEM gold standard.
GaussianFluent: Gaussian Simulation for Dynamic Scenes with Mixed Materials: GaussianFluent supplements surface-only 3DGS by filling interiors with realistic textured Gaussians using generative models. It integrates a stabilized, parallelized Continuous Damage Material Point Method (CD-MPM) into the simulation, enabling 3DGS to realistically simulate brittle fracture, cutting, and bullet penetration in mixed materials at real-time speeds with exposed internal structures.
Generative Diffusion Priors for 3D Mapping of the Dark Universe: This paper transforms the highly ill-posed cosmological inverse problem of "reconstructing the 3D dark matter distribution from weak gravitational lensing observations" into a diffusion model posterior sampling task. It utilizes N-body simulations to construct the Conicus3D light-cone dataset and trains a redshift-conditioned 2D diffusion prior. By coupling this data-driven prior with a differentiable weak lensing forward model using a modified DAPS algorithm, the approach significantly improves 3D/2D reconstruction correlations and power spectrum fidelity compared to Wiener filtering and Neural Ensemble baselines on simulated JWST COSMOS-Web surveys.
Learning to Solve PDEs on Neural Shape Representations: This paper learns the "normal extension" step—the most critical component of the classic Closest Point Method (CPM)—using a lightweight, geometrically-conditioned neural operator. This enables solving surface PDEs directly on neural surface representations (SNS / SDF / Occupancy Fields / Point Clouds / Gaussian Splatting) without mesh extraction or per-instance optimization. The pipeline is fully differentiable and, after being trained once on a single example shape (Spike), generalizes to unseen shapes, topologies, and input functions with accuracy comparable to CPM.
Lens Component Deletion based on Differentiable Ray Tracing: To meet the miniaturization and cost-reduction needs of micro-optical lenses, an "automated lens deletion" pipeline is proposed. It uses a contribution metric to automatically identify the least significant lens in a system, applies a deletion loss to gradually flatten and thin it until safe removal, and employs a differentiable PSF estimation based on Rayleigh-Sommerfeld diffraction theory. This allows for joint optimization of the simplified lens and a post-processing restoration network, maintaining imaging quality comparable to the original system even after removing a lens component.
UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching: UniPixie reformulates "inferring physical properties from vision" from deterministic point estimation to controllable probabilistic distribution modeling. Using a shared Perceiver-IO encoder and a conditional Flow Matching decoder, it generates physical parameters along a "soft-to-hard" continuum from a single visual input. It is the first unified architecture to simultaneously produce plug-and-play parameters for MPM, LBS, and Spring-Mass solvers, reducing Young's modulus error by over 50% compared to the strongest deterministic baseline.