⚛️ Physics & Scientific Computing

📷 CVPR2025 · 7 paper notes

📌 Same area in other venues: 📷 CVPR2026 (2) · 🔬 ICLR2026 (69) · 🧪 ICML2026 (33) · 🤖 AAAI2026 (15) · 🧠 NeurIPS2025 (57) · 📹 ICCV2025 (2)

Accurate Differential Operators for Hybrid Neural Fields

This paper reveals that gradients and curvatures computed via automatic differentiation in hybrid neural fields (e.g., Instant NGP) suffer from severe high-frequency noise. It proposes a post-processing differential operator based on local polynomial fitting and a self-supervised fine-tuning method, reducing gradient and curvature errors by 4x, which significantly eliminates artifacts in rendering and physical simulations.

ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks

This paper proposes Adaptive Threshold Pruning (ATP) to adaptively prune low-information data features prior to quantum data encoding. By optimizing thresholds via L-BFGS-B, ATP achieves the highest accuracy in binary classification tasks across four datasets (MNIST, FashionMNIST, CIFAR, PneumoniaMNIST) while significantly reducing entanglement entropy.

DiffFNO: Diffusion Fourier Neural Operator

This paper proposes DiffFNO, which integrates the Weighted Fourier Neural Operator (WFNO) with a diffusion framework for arbitrary-scale super-resolution. It preserves critical high-frequency components through Mode Rebalancing, fuses frequency-domain and spatial-domain features using a Gated Fusion Mechanism, and accelerates inference with an adaptive-step ODE solver, outperforming existing methods by 2-4 dB in PSNR across multiple benchmarks.

Improve Representation for Imbalanced Regression through Geometric Constraints

This work is the first to study representation space uniformity in deep imbalanced regression (DIR). It proposes two geometric constraints, namely enveloping loss and homogeneity loss, to ensure that regression representations are uniformly distributed on the hypersphere. It also designs a surrogate-driven representation learning (SRL) framework to integrate global geometric constraints into mini-batch training, achieving SOTA on several DIR tasks such as age estimation.

KAC: Kolmogorov-Arnold Classifier for Continual Learning

First to apply Kolmogorov-Arnold Networks (KAN) to continual learning. By replacing B-splines with Radial Basis Functions (RBF) to construct the classifier KAC, consistent and significant performance gains are achieved across multiple continual learning methods (up to +20.70% on CUB200 40-step) with only 0.23M additional parameters.

Learning Phase Distortion with Selective State Space Models for Video Turbulence Mitigation

MambaTM is proposed as the first Mamba-based video atmospheric turbulence mitigation network. It reparameterizes the phase distortion traditionally represented by Zernike polynomials into Latent Phase Distortion (LPD) via a VAE, using LPD to guide the state transitions of SSMs. While maintaining linear complexity and a global receptive field, it achieves state-of-the-art restoration quality and nearly 2× inference speedup (55.4 FPS vs 32.7 FPS).

Towards Faithful Multimodal Concept Bottleneck Models

Proposes f-CBM, a faithful multimodal Concept Bottleneck Model framework based on CLIP. By jointly addressing concept detection accuracy and information leakage via a differentiable leakage loss and a Kolmogorov-Arnold Network prediction head, it achieves the optimal trade-off among task accuracy, concept detection, and leakage.