B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

References

Page 1

	Year	Citations
Advances in Neural Information Processing Systems 28 Peter Flach, Meelis Kull Intelligent Information ProcessingComputational NeuroscienceComputer ScienceNeurocomputersNeural Information Processing	2015	14.6K
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Maziar Raissi, Paris Perdikaris, George Em Karniadakis Journal of Computational Physics EngineeringPde-constrained OptimizationDeep Learning FrameworkAi FoundationInverse Problems	2018	14.4K
Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning Yarin Gal, Zoubin Ghahramani arXiv (Cornell University)	2015	4.1K
Data-driven discovery of partial differential equations Samuel Rudy, Steven L. Brunton, Joshua L. Proctor, Science Advances	2017	1.5K
Weight Uncertainty in Neural Networks Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, arXiv (Cornell University) Artificial IntelligenceEngineeringMachine LearningData ScienceWeight Uncertainty	2015	1.3K
Physics-informed neural networks for high-speed flows Zhiping Mao, Ameya D. Jagtap, George Em Karniadakis Computer Methods in Applied Mechanics and Engineering Unsteady FlowEngineeringPhysic Aware Machine LearningFluid MechanicsFlow Physic	2019	1.1K
Practical Variational Inference for Neural Networks Alex Graves	2011	1.1K
A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems Xuhui Meng, George Em Karniadakis Journal of Computational Physics Model OptimizationEngineeringMachine LearningData SciencePde-constrained Optimization	2019	633
Data-driven science and engineering: machine learning, dynamical systems, and control Thomas Peters Contemporary Physics Artificial IntelligenceData-driven PlanningData-driven OptimizationData-driven ScienceEngineering	2019	622
Machine learning of linear differential equations using Gaussian processes Maziar Raissi, Paris Perdikaris, George Em Karniadakis Journal of Computational Physics Gaussian ProcessMachine Learning	2017	571

Page 1