physics-informed

This paper benchmarks six generative deep learning models for nowcasting aerosol particle size distributions from PM2.5 and meteorological data, finding that physics-guided flow matching performs best, with a reduced general dynamic equation constraint lowering CRPS by 5.5–11.5% at the training site.

本文提出了一种物理信息图表示与约束专用评论家相结合的安全深度强化学习框架（LDCPPO），用于配电网中社区电池储能的协调控制，以缓解电压和线路越限问题并降低运行成本，实验显示关键电压违规程度最高降低80.5%。

A new paper introduces LPDS, a generative inverse modeling framework that maps desired fusion outcomes and target configurations to optimized laser pulse shapes for inertial confinement fusion, using multi-objective physics-constrained training and control mechanisms like constraint-conditioning and inpainting.

提出了 Cautious ConFIG，一个三行扩展的优化算法，修复了 ConFIG 与 Adam 等自适应优化器结合时破坏无冲突梯度方向的问题，并在多个 PINN 基准上取得一致性能提升。

提出了 Differentiable Diffusion Conditioning (D2C)，通过使冻结条件扩散模型的确定性反向去噪链对条件信号可微，将其转化为基于梯度的端到端优化器，用于离线模型优化，在激光脉冲优化、数据中心调度及 Design-Bench 任务上取得最佳结果。

一篇探讨深度强化学习在高超声速/高速流动控制中应用前景与挑战的综述性论文，讨论了可解释性、仿真保真度、数据效率及工程落地路径等关键约束。

This paper proposes a domain-informed adaptive sampling framework for PINNs in metal additive manufacturing, using a conditional Flow Matching model to target high-residual collocation regions, achieving a 62.1% average reduction in relative L2 error over state-of-the-art baselines under an identical collocation budget.

The paper introduces ORACLE, an optimizer-relative constrained learning method that assesses constraint compatibility on the post-optimizer update rather than the objective or gradient, showing improvements over native optimizers in 94% of configurations across eight PDE benchmarks.

This paper proposes a physics-informed world model that generates dynamics from the gradient of a learned energy via a fixed reversible operator, with one-way energy dissipation and structured parameter interventions. Across electromagnetic, particle-in-cell, and shallow-water systems, ~9k-parameter models achieve stable 100x-horizon rollouts, correct counterfactual responses, and out-of-distribution refusal when no intervention map is supplied, far outperforming unstructured equal-capacity baselines.

This arXiv paper identifies 'geometry–physics confounding' as a unified failure mechanism for learning PDEs across varying domains, and proposes a de-confounding framework using geometry-induced coefficient fields and geometry-complete candidate libraries that dramatically improves operator learning and equation discovery accuracy.

A single-authored arXiv paper proposes a data-free physics-informed neural operator for level-set interface advection, trained only on transport residuals and a geometric constraint without any reference solutions, outperforming supervised baselines in area conservation while quantifying the accuracy cost of refusing labels.

KernelOnet proposes an interpretable neural operator framework that explicitly incorporates kernel functions in place of trunk networks, offering data-driven, physics-informed, and hybrid kernel strategies for PDE solving with higher accuracy and fewer parameters than DeepONet.

The paper introduces the Transformer-Mamba for Flow Field (TM4FF) framework, a physics-constrained operator learning model that enhances accuracy and robustness in computational fluid dynamics simulations through innovations like Residual Wavelet Mamba and physics-informed loss.

The paper proposes a Physics Informed Recurrent Neural Network (PIRNN) that predicts unobservable physical variables to improve time series forecasting in physical processes, demonstrated through groundwater level predictions.

Drift Field Net (DFN) is a deep neural network that predicts ocean surface flow fields from satellite observations, using a two-stage training strategy to improve particle trajectory accuracy. It reduces positioning errors by 20 km in 7-day forecasts compared to operational models, with further gains from Lagrangian fine-tuning.

The paper introduces Physics-Informed Conformal Prediction (PI-CP), a framework that embeds PDE residuals into conformal prediction to provide distribution-free, spatially adaptive uncertainty quantification for neural operators like the Fourier Neural Operator (FNO).

The paper introduces Fundamental Dynamical Units (FDUs) as composable primitives for physics-informed structural inference in networked dynamical systems, using neural ODEs to recover interaction structures from perturbation time-series data.

GenONet is a novel deep learning architecture that uses Deep Operator Networks within a Generative Adversarial Network framework for high-resolution precipitation nowcasting, producing sharp and physically consistent forecasts.

The paper proposes a self-augmented diffusion guidance method to incorporate physical laws into diffusion models, reducing deviations from true dynamics and enabling faster generation.

This paper introduces a physics-integrated operator learning framework using a feed-forward Gaussian splatting representation to directly incorporate PDE operators, reducing errors in long-horizon autoregressive predictions for spatiotemporal systems.