Science & medicine for expert learners
160 resources · page 5 of 5
SpyTorch
SpyTorch GitHub repo: tutorial on surrogate gradient learning in spiking neural networks.
State Council Opinions on Deepening the "AI+" Action (国务院关于深入实施"人工智能+"行动的意见)
by State Council of the People's Republic of China
China State Council opinions (August 2025) on deepening the AI+ action across the economy.
TerraTorch
by TorchGeo
TerraTorch GitHub repo: toolkit for fine-tuning geospatial foundation models.
The Well dataset
Polymathic AI The Well GitHub repo: 15TB collection of physics simulation datasets for machine learning.
TorchANI
by AIQM
TorchANI GitHub repo: library for training and using ANI neural network potentials for molecules.
TxGNN
by Artificial Intelligence for Medicine and Science @ Harvard Zitnik Lab
Harvard TxGNN GitHub repo: graph model for zero-shot prediction of drug repurposing.
UNI (Mahmood Lab) pathology foundation model
Mahmood Lab UNI GitHub repo: pathology foundation model (Nature Medicine) with weights and embeddings.
Uni-Mol
by DeepModeling
Uni-Mol GitHub repo (DP Technology): 3D molecular pretraining models for property prediction and docking.
Wildlife Insights
Wildlife Insights: platform using machine learning to identify species in camera-trap images for conservation.
Zooniverse
by Zooniverse
Zooniverse: citizen-science platform whose volunteer labels support research projects and train ML models.
AIFS paper (ECMWF)
arXiv paper (2024) describing AIFS, ECMWF's data-driven weather forecasting system.
Aurora
by Cristian Bodnar et al. (Microsoft)
arXiv HTML version of the Aurora paper (2024) on a foundation model of the atmosphere.
ClimaX
by Tung Nguyen et al. (UCLA and Microsoft Research)
ICML 2023 paper ClimaX: a foundation model for weather and climate.
DeepONet (Lu et al.)
by Lu Lu et al. (Brown University)
arXiv paper (2019) introducing DeepONet for learning nonlinear operators of differential equations.
Fourier Neural Operator (Li et al.)
arXiv paper (2020) introducing the Fourier Neural Operator for learning parametric PDE solutions.
GenCast paper
by Ilan Price et al. (Google DeepMind)
arXiv paper (2023) introducing GenCast, a diffusion-based ensemble model for medium-range weather forecasting.
GraphCast paper
by Remi Lam et al. (Google DeepMind)
arXiv paper (2022) introducing GraphCast, a graph neural network for medium-range global weather forecasting.
KAN: Kolmogorov-Arnold Networks
arXiv paper (2024) introducing Kolmogorov-Arnold Networks (KAN) with learnable activation functions on edges.
Learning to Simulate Complex Physics with Graph Networks (GNS)
by Alvaro Sanchez-Gonzalez et al. (Google)
arXiv paper (2020) introducing Graph Network-based Simulators (GNS) for learning complex particle physics.
MeshGraphNets
by Tobias Pfaff et al. (Google)
arXiv paper (2020) introducing MeshGraphNets for learning mesh-based physical simulation.
Neural Operator: Learning maps between function spaces
by Nikola Kovachki et al. (Caltech)
arXiv paper (2021) "Neural Operator: Learning Maps Between Function Spaces", the general framework covering Fourier and graph neural operators for PDEs.
PaiNN
by Kristof T. Schütt et al. (Technische Universität Berlin)
arXiv HTML version of the PaiNN paper (2021) on equivariant message passing for molecular property prediction.
Physics-informed deep learning (Raissi et al.)
by Maziar Raissi et al. (Brown University)
arXiv paper (2017) introducing physics-informed neural networks (PINNs) for data-driven solutions of nonlinear PDEs.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
by Kristof T. Schütt et al. (Technische Universität Berlin)
arXiv paper (2017) introducing SchNet, a continuous-filter convolutional network for modelling quantum interactions in molecules.
SchNet: a deep learning architecture for molecules and materials
by Kristof T. Schütt et al. (Technische Universität Berlin)
arXiv paper (2017) "SchNet - a deep learning architecture for molecules and materials", using continuous-filter convolutions to predict molecular energies and forces.
Anemoi
Anemoi documentation: ECMWF-led framework for building data-driven weather forecasting models, from data to inference.
FAIR-Chem
FAIR Chemistry documentation: Meta's open datasets, models and workflows for atomistic chemistry and materials.
Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI (NBER w33777)
by National Bureau of Economic Research (NBER)
NBER working paper w33777 by Humlum and Vestergaard on early labour-market effects of AI chatbots, using Danish data.
NASA Autonomy Verification & Validation Roadmap and Vision 2045
by NASA
NASA technical report: Autonomy Verification and Validation Roadmap and Vision 2045 for learning-enabled aircraft systems.
NeuralOperator
NeuralOperator documentation: PyTorch library implementing Fourier and other neural operators for learning PDE solutions.
NVIDIA PhysicsNeMo Framework documentation
by NVIDIA
NVIDIA PhysicsNeMo documentation: framework for physics-informed ML and neural operators for simulation.
SciML / DiffEqFlux documentation
by SciML Open Source Scientific Machine Learning
DiffEqFlux.jl documentation: Julia library combining differential equation solvers with neural networks for scientific ML.