Proposes a non-intrusive correction method using VAE latent space adaptation to reduce discrepancies between CFD predictions and experiments for open tip clearance flow in compressor cascades, trained on 166 parametrically sampled CFD total pressure loss fields.
A region-held-out evaluation protocol for hydrogen embrittlement detection in 316L stainless steel, preventing data leakage from same specimen regions, comparing texture features vs deep learning for SEM micrograph classification.
This high-energy physics work develops a Parnassus conditional flow matching surrogate for the CLD detector, emulating full detector simulation and event reconstruction to address computationally expensive collider studies.
NewSee LICENSE fileHigh-energy physicsParticle detector simulationGenerative surrogate model
This paper investigates neural network solvers for infinity and p-Laplace problems in nonlinear analysis, applying PINNs and DeepONets to 2D and 3D domains with p values ranging from 2 to 1000.
A physics-informed deep learning framework performs single-snapshot 3D phase-only positioning in narrowband phase-coherent distributed MIMO networks under two-ray electromagnetic propagation.
This paper develops a surrogate-assisted stochastic multiobjective framework for photovoltaic hosting-capacity planning, jointly optimizing penetration and total electrical loss under reliability constraints.
This paper applies meta-adaptive filtering to active noise control when acoustic paths vary over time, constructing physics-informed optimizer features from a secondary-path estimate to support online filter updates.
This study evaluates five deep learning architectures for univariate significant wave height forecasting using buoy observations. Across a 47-buoy, 37-year evaluation corpus, the model families converge to nearly identical performance, with an between-family standard deviation of 0.0014 square meters.
This paper presents differentiable and hardware-accelerated implementations of the machine-learning surrogate models mlgw and mlgw-bns for gravitational-wave waveform modeling, enabling JIT compilation, vector parallelization, GPU acceleration, and automatic differentiation within JAX.
This paper studies gradient conflicts and multi-task optimization in Physics-Informed Neural Networks, with implications for more stable convergence in stiff and high-frequency partial differential equations.
Reveals compatibility conditions under which multi-symplectic Hamiltonian PDEs regularized by Brinkman-type penalization retain a multi-conformal symplectic structure, yielding exact local conservation laws for structure-preserving operator learning on complex geometries.
DNNsolver introduces a deep learning-based approach for modeling polarization-sensitive diffractive neural networks, addressing the limitation of thin element approximation in capturing intra-layer electromagnetic coupling for improved accuracy and efficiency in high-density optical computing.
A physics-informed GNN-based foundation model for AC-OPF, pretrained across 54 grid topologies (500-4,000 buses), achieving 2.45% zero-shot generation-cost error on 10,000-bus systems.
NewSee LICENSE fileAC-OPFGraph Neural NetworkPower System Optimization
This study develops a patient-specific image-based computational modeling framework to evaluate the hemodynamic impact of mitral and aortic valve repair in five LVAD-supported patients, addressing valve dysfunction threats to long-term LVAD therapy success.
LeafNet algorithm reformulates hyperbolic conservation laws as a coupled system with smooth solutions and approximates smooth functions via physics-informed neural networks, enabling accurate computation of multidimensional hyperbolic PDEs without diffusion.
PR-Smoother is an amortized smoother for prescribed-simulator data assimilation, supporting non-Gaussian posterior estimation over physical state variables in high-dimensional simulators with calibration compatibility.
RFlash uses differentiable acoustic simulation and radiance field decomposition to reduce bone-induced shadow artifacts in ultrasound imaging, improving symmetric assessment of fetal brain hemispheres.
Proposes SO(3) Equivariant Neural Kalman Networks (SENK) with response-state cascade to improve transferable vibrational spectral prediction across chemical spaces using equivariant transformer backbone.
SHRAV is an architecture-independent computational framework for physical modeling and inverse design, featuring a state-continuation core with declared reuse boundaries and explicit roles for learned evolution.
