Year  Title  Author 
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): Datadriven Solutions of Nonlinear Partial Differential Equations 
M Raissi, P Perdikaris, GE Karniadakis

2017

Physics Informed Deep Learning (Part II): Datadriven 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 Highenergy Physics Exploration with Deep Learning 
D Ojika, D Acosta, A Gordon

2017

Scene Physics Acquisition via Visual Deanimation 
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 HighEnergy 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 highenergy physics 
P Baldi, K Cranmer, T Faucett, P Sadowski, D Whiteson

2016

Jet Flavor Classification in HighEnergy Physics with Deep Neural Networks 
D Guest, J Collado, P Baldi, SC Hsu, G Urban

2016

Jet Substructure Classification in HighEnergy 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 manybody 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
