进化算法加持LLM，让物理信息神经网络设计告别试错，智能更高效。
arXiv:2607.15560v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) are unusually sensitive to interacting choices of architect…
李对称性赋能科学计算，新型求解器提升PDE学习精度与效率。
arXiv:2510.25731v2 Announce Type: replace-cross Abstract: Initial-boundary value problems (IBVPs) provide the essential framework for modelling a wide…
用可证深度学习为灌注物理信息神经网络引入残差不确定性量化，让医学影像AI诊断更可靠。
arXiv:2603.09359v2 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) have shown promise in addressing the ill-posed deconvolut…
双阶段物理信息神经网络破解系数跳变反问题，统计混合模型加持，科学计算新突破。
arXiv:2510.14656v2 Announce Type: replace-cross Abstract: This work proposes a two-stage physics-informed deep learning framework that combines neural…
被ICASSP 2026接收的PINN训练新方法，提出无模块化冲突避免策略，提升泛化能力。
arXiv:2606.20156v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have become a powerful framework for solving PDEs by embeddin…
将Port-Hamiltonian系统与Kolmogorov-Arnold网络融合，为可解释、保物理规律的神经网络设计提供新方案。
arXiv:2606.14708v1 Announce Type: cross Abstract: Data-driven machine learning approaches have become increasingly attractive for nonlinear system ide…
引入损失条件机制优化PINNs训练，高效求解参数化偏微分方程族
arXiv:2606.04420v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of ODEs and PDEs by minimising a weight…
在谱空间融合物理信息的扩散模型，提升科学计算中生成数据的物理一致性与精度。
arXiv:2602.09708v2 Announce Type: replace-cross Abstract: We propose physics-informed spectral diffusion (PISD), a methodology that combines generativ…
硬约束仿射神经网络新架构，精准建模复杂物理约束，提升模型可解释性与泛化能力。
arXiv:2605.24437v1 Announce Type: new Abstract: We present a novel framework for embedding hard constraint satisfaction into neural network (NN) archi…
数字孪生参数估计新方法：加权流匹配融合物理信息非线性滤波，提升推断精度
arXiv:2605.17146v1 Announce Type: cross Abstract: Digital twins (DTs) rely on continuous synchronization between physical systems and their virtual co…