yingtaoluo / Probabilistic-density-network
Probability-density-based deep learning paradigm for the fuzzy design of functional metastructures. Research AAAS & NeurIPS ML4PS.
☆20Updated 2 years ago
Alternatives and similar repositories for Probabilistic-density-network:
Users that are interested in Probabilistic-density-network are comparing it to the libraries listed below
- All the codes and data used in "Inverse design of soft materials via a deep-learning-based evolutionary strategy", by G. M. Coli, E. Boat…☆11Updated 3 years ago
- Deep Reinforcement Learning for Optical Design☆22Updated 3 years ago
- "Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy…☆29Updated 3 years ago
- ☆28Updated last year
- X-ray diffraction denoising using deep convolutional neural networks☆11Updated 11 months ago
- Python package for modeling structural color in colloidal systems☆16Updated this week
- Repo for paper "Inverse deep learning methods and benchmarks for artificial electromagnetic material design"☆15Updated 2 years ago
- COMSOL implementation of the mesoscopic boundary conditions for nanoscale electromagnetism☆31Updated 4 years ago
- Global optimization based on generative neural networks☆105Updated 2 years ago
- Neural Inverse Design of Nanostructures☆39Updated last year
- ☆28Updated 2 years ago
- Script for generate a 2D geometry based on a Voronoi diagram in COMSOL with Livelink for MATLAB☆15Updated 2 years ago
- Numerical simulation of the High Harmonic Generation process accounting for Quantum mechanic, Supersonic flow physics, Plasma physics and…☆16Updated 4 years ago
- BEM solver for Maxwell equations☆15Updated 2 years ago
- Code base for the graph neural network-based polygrain microstructure property prediction project☆41Updated 2 years ago
- Rigorous coupled wave analysis and PWEM implemented in short readable python codes☆35Updated 3 years ago
- Here, we use Deep SHAP (or SHAP) to explain the behavior of nanophotonic structures learned by a convolutional neural network (CNN). Refe…☆21Updated 2 years ago
- 1D model written in Python which solves the semiconductor Poisson-Drift-Diffusion equations using finite-differences.☆27Updated 3 years ago
- Neural operator surrogates for electromagnetic inverse design☆33Updated last year
- ☆11Updated 2 years ago
- 📐 Workshop material for optical inverse design and automatic differentiation☆105Updated 4 years ago
- 3 physical problems + multiple ML architectures benchmarking☆11Updated 2 years ago
- A python code to calculate the Brownian motion of colloidal particles in a time varying force field.☆20Updated 3 months ago
- 2D and 3D scalar finite-difference frequency-domain solver of the scattering matrix with the recursive Green's function method.☆14Updated 2 years ago
- Band diagram and Field of 2D photonic cristal☆41Updated 3 years ago
- Machine learning molecule graphs from atomic force microscopy images.☆12Updated last year
- A hybrid data- and physics-augmented CNN that predicts EM field distributions with ultrafast speed and high accuracy for entire classes o…☆19Updated 2 years ago
- Physics-Informed Neural Networks for Solving Multiscale Mode-Resolved Phonon Boltzmann Transport Equation☆19Updated 3 years ago
- Software for analysis of time-resolved and transient absorption data from pump-probe experiments (1D) and 2D-IR☆13Updated this week
- An electromagnetic solver capable of simulating and optimizing 1D (thin-layer) structures via the semi-analytical transfer matrix method.…☆16Updated 3 years ago