Deep Learning Publication Navigator - subtopic: physics


Year TitleAuthor
2017   Deep Learning the Physics of Transport Phenomena  AB Farimani, J Gomes, VS Pande
2017   Deep learning and neutrino physics  GN Perdue, T Golan, A Himmel, E Niner, F Psihas
2017   Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations  M Raissi, P Perdikaris, GE Karniadakis 
2017   Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations  M Raissi, P Perdikaris, GE Karniadakis 
2017   Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning  P Sadowski, B Radics, Y Yamazaki, P Baldi
2017   Importance and construction of features in identifying new physics signals with deep learning  CW Loh, R Zhang, YH Xu, ZQ Qian, SC Chen, HY Long 
2017   Accelerating High-energy Physics Exploration with Deep Learning  D Ojika, D Acosta, A Gordon
2017   Scene Physics Acquisition via Visual De-animation  J Wu, E Lu, P Kohli, B Freeman, J Tenenbaum 
2017   Deep learning for teaching university physics to computers  JP Davis, WA Price
2017   Adversarial learning to eliminate systematic errors: a case study in High Energy Physics  V Estrade, C Germain, I Guyon, D Rousseau 
2017   Classification without labels: Learning from mixed samples in high energy physics  EM Metodiev, B Nachman, J Thaler
2016   Image Processing, Computer Vision, and Deep Learning: new approaches to the analysis and physics interpretation of LHC events  A Schwartzman, M Kagan, L Mackey, B Nachman
2016   Physics Simulation Games 4  J Renz, X Ge
2016   Learning to Poke by Poking: Experiential Learning of Intuitive Physics  P Agrawal, A Nair, P Abbeel, J Malik, S Levine
2016   A Differentiable Physics Engine for Deep Learning in Robotics  J Degrave, M Hermans, J Dambre
2016   Statistical physics of linear and bilinear inference problems  C Schülke
2016   Interaction Networks for Learning about Objects, Relations and Physics  P Battaglia, R Pascanu, M Lai, DJ Rezende
2016   Parameterized Machine Learning for High-Energy Physics  P Baldi, K Cranmer, T Faucett, P Sadowski, D Whiteson
2016   Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks  E Racah, S Ko, P Sadowski, W Bhimji, C Tull, SY Oh
2016   Parameterized neural networks for high-energy physics  P Baldi, K Cranmer, T Faucett, P Sadowski, D Whiteson
2016   Jet Flavor Classification in High-Energy Physics with Deep Neural Networks  D Guest, J Collado, P Baldi, SC Hsu, G Urban
2016   Jet Substructure Classification in High-Energy Physics with Deep Neural Networks  P Baldi, K Bauer, C Eng, P Sadowski, D Whiteson
2016   Physics Learning Review: Autonomy Support, Gender Gap Reduction, and Measuring Mathematics Reasoning Ability  JC Lear
2016   Exploring many-body physics with deep networks  G Torlai, J Carrasquilla, D Schwab, R Melko
2015   Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem  T Hamelryck, W Boomsma, J Ferkinghoff
2015   Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning  J Wu, I Yildirim, JJ Lim, B Freeman, J Tenenbaum