Tag: Deep Learning

Materials

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.

ScientificML

Proposes Interface Autoencoder (IAE) for learning neural operators on geometry-dependent PDEs, representing interface-function states in a fixed domain via finite projection codes.

Multibody

HuggingFace blog tutorial on using NVIDIA Warp physics framework and MjWarp bridge for robotics simulation, covering rigid body dynamics and contact mechanics in learning workflows.

Multibody

A Transformer-based warm-starting approach for non-convex trajectory optimization in cislunar rendezvous, proximity operations, and docking scenarios.

Structural

DeepFEAv2 applies deep learning surrogate models to transient finite element analysis on unstructured meshes, enabling joint prediction of node-based and element-based outputs to reduce computational cost in structural mechanics.

CFD

A 36M-parameter UNet-based model for solving Navier-Stokes equations using a point-wise coupling strategy for high-accuracy flow field prediction.

ScientificML

This paper proposes PIRNN to enhance interpretability and accuracy of time series forecasting for physical processes by incorporating physical knowledge from historical data during training.

Physics Simulation

NVIDIA PhysicsNeMo v2.2.2 release fixes documentation alignment with the PyPi package name, improving installation accuracy.

PINN Model

A HuggingFace repository hosting a Physics-Informed Neural Network model for hydrostatic fluid mechanics computations, applying physics-based constraints to deep learning approaches.

CFD

Proposes a physics-guided hybrid architecture combining UNet and FNO, integrating multi-modal Sentinel-1/2 remote sensing with shallow water equation constraints for end-to-end flood prediction.

Thermal

Therm-FM adapts a pretrained PDE foundation model to steady-state and transient 3D-IC thermal simulation, reducing data-generation costs for cross-design reuse.

Electromagnetics

DNNsolver introduces a deep learning approach for modeling polarization-sensitive diffractive neural networks, addressing the challenge of accurate and efficient electromagnetic coupling simulation within cascaded diffractive layers.

PDE Solving

DeepXDE v1.15.0 released with L-LAAF activation function support for JAX backend and Apple MPS GPU acceleration for PyTorch backend, improving deep learning-based PDE solving performance.

Scientific Computing

NeuralOperator 2.0.0 major release introduces Codano model, improves documentation, and enhances usability for training Neural Operators in PyTorch.