Papers Physical Intuition
“Physical Intuition” 태그가 달린 논문 51편 · 필터 해제
Towards understanding how attention mechanism works in deep learning
Attention mechanism has been extensively integrated within mainstream neural network architectures, such as Transformers and graph attention networks. Yet, its underlying working principles remain somewhat elusive. What …
Deep LearningGraph AttentionMetric LearningPhysical IntuitionA Note on Spectral Map
In molecular dynamics (MD) simulations, transitions between states are often rare events due to energy barriers that exceed the thermal temperature. Because of their infrequent occurrence and the huge number of degrees o…
Physical IntuitionAutomated design of nonreciprocal thermal emitters via Bayesian optimization
Nonreciprocal thermal emitters that break Kirchhoff's law of thermal radiation promise exciting applications for thermal and energy applications. The design of the bandwidth and angular range of the nonreciprocal effect,…
Bayesian OptimizationPhysical IntuitionInvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors
The discovery of new superconducting materials, particularly those exhibiting high critical temperature ($T_c$), has been a vibrant area of study within the field of condensed matter physics. Conventional approaches prim…
Physical IntuitionOptimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model
Accurately measuring the cycle lifetime of commercial lithium-ion batteries is crucial for performance and technology development. We introduce a novel hybrid approach combining a physics-based equation with a self-atten…
Physical IntuitionMechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
Solving mechanics problems using numerical methods requires comprehensive intelligent capability of retrieving relevant knowledge and theory, constructing and executing codes, analyzing the results, a task that has thus …
Language ModelingLanguage ModellingLarge Language ModelPhysical IntuitionPhysics-tailored machine learning reveals unexpected physics in dusty plasmas
Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb forces mediated by the surrounding plasma…
Physical Intuitionscientific discoveryConstructing Custom Thermodynamics Using Deep Learning
One of the most exciting applications of artificial intelligence (AI) is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetr…
Deep LearningPhysical Intuitionscientific discoveryGeneralizing Adam to Manifolds for Efficiently Training Transformers
One of the primary reasons behind the success of neural networks has been the emergence of an array of new, highly-successful optimizers, perhaps most importantly the Adam optimizer. It is widely used for training neural…
Physical IntuitionBayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning
Recent progress in machine learning methods, and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs), have propelled automated and autonomous microscopies to the forefront of attent…
Active LearningBayesian InferenceBIG-bench Machine LearningGaussian Processes+1Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence …
AnachronismsAnalogical SimilarityAnalytic EntailmentCausal Judgment+69Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses
With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn compl…
Graph AttentionPhysical IntuitionAnalytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression
The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra which are obtained from complex numerical models of radiative transfer. Alter…
Physical IntuitionregressionSymbolic RegressionScaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis o…
Abstract AlgebraAnachronismsAnalogical SimilarityAnalytic Entailment+143Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial
Deep Neural Networks (DNN) and especially Convolutional Neural Networks (CNN) are a de-facto standard for the analysis of large volumes of signals and images. Yet, their development and underlying principles have been la…
Dimensionality ReductionPhysical IntuitionAct the Part: Learning Interaction Strategies for Articulated Object Part Discovery
People often use physical intuition when manipulating articulated objects, irrespective of object semantics. Motivated by this observation, we identify an important embodied task where an agent must play with objects to …
Motion SegmentationPhysical IntuitionWhy is AI hard and Physics simple?
We discuss why AI is hard and why physics is simple. We discuss how physical intuition and the approach of theoretical physics can be brought to bear on the field of artificial intelligence and specifically machine learn…
BIG-bench Machine LearningLearning TheoryPhysical IntuitionAutomated Optical Multi-layer Design via Deep Reinforcement Learning
Optical multi-layer thin films are widely used in optical and energy applications requiring photonic designs. Engineers often design such structures based on their physical intuition. However, solely relying on human exp…
Deep Reinforcement LearningPhysical Intuitionreinforcement-learningReinforcement Learning+2Leveraging 2D Data to Learn Textured 3D Mesh Generation
Numerous methods have been proposed for probabilistic generative modelling of 3D objects. However, none of these is able to produce textured objects, which renders them of limited use for practical tasks. In this work, w…
Physical IntuitionAdvances in Bayesian Probabilistic Modeling for Industrial Applications
Industrial applications frequently pose a notorious challenge for state-of-the-art methods in the contexts of optimization, designing experiments and modeling unknown physical response. This problem is aggravated by limi…
Physical Intuition