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Physical Intuition

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Showing 1–35 of 35 papers

TitleStatusHype
Training Compute-Optimal Large Language ModelsCode6
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
Constructing Custom Thermodynamics Using Deep LearningCode1
Physics-Inspired Distributed Radio Map EstimationCode1
InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductorsCode1
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledgeCode1
Leveraging 2D Data to Learn Textured 3D Mesh GenerationCode1
Generalizing Adam to Manifolds for Efficiently Training TransformersCode1
Can Theoretical Physics Research Benefit from Language Agents?—0
Combining Machine Learning and Physics to Understand Glassy Systems—0
Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial—0
Generalizable Features From Unsupervised Learning—0
Guiding Physical Intuition with Neural Stethoscopes—0
Large Language Models and Mathematical Reasoning Failures—0
Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses—0
Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery—0
Advances in Bayesian Probabilistic Modeling for Industrial Applications—0
Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression—0
A Note on Spectral Map—0
Automated design of nonreciprocal thermal emitters via Bayesian optimization—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Biophysics at the coffee shop: lessons learned working with George Oster—0
Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model—0
Portfolio Cuts: A Graph-Theoretic Framework to Diversification—0
Towards understanding how attention mechanism works in deep learning—0
What is the Machine Learning?—0
Why is AI hard and Physics simple?—0
A Complete Recipe for Stochastic Gradient MCMC—0
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly DetectionCode0
Learning Physical Intuition of Block Towers by ExampleCode0
Physics-tailored machine learning reveals unexpected physics in dusty plasmasCode0
Meta-Learning for Stochastic Gradient MCMCCode0
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object StackingCode0
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational AutoencodersCode0
Automated Optical Multi-layer Design via Deep Reinforcement LearningCode0
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