{"list":"other","total":59,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"2010.08895","title":"Fourier Neural Operator for Parametric Partial Differential Equations","created":"2020-10-18","cited_by_count":2908},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1910.03193","title":"DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators","created":"2019-10-08","cited_by_count":2018},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"2202.11214","title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","created":"2022-02-22","cited_by_count":776},{"id":"1802.08219","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","created":"2018-02-22","cited_by_count":746},{"id":"2003.03485","title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","created":"2020-03-07","cited_by_count":734},{"id":"1906.01563","title":"Hamiltonian Neural Networks","created":"2019-06-04","cited_by_count":712},{"id":"2101.03164","title":"E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials","created":"2021-01-08","cited_by_count":708},{"id":"2006.10503","title":"SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks","created":"2020-06-18","cited_by_count":607},{"id":"2103.10974","title":"Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets","created":"2021-03-19","cited_by_count":540},{"id":"2108.08481","title":"Neural Operator: Learning Maps Between Function Spaces","created":"2021-08-19","cited_by_count":414},{"id":"2202.03376","title":"Message Passing Neural PDE Solvers","created":"2022-02-07","cited_by_count":386},{"id":"2001.04385","title":"Universal Differential Equations for Scientific Machine Learning","created":"2020-01-13","cited_by_count":353},{"id":"1710.09668","title":"PDE-Net: Learning PDEs from Data","created":"2017-10-26","cited_by_count":311},{"id":"1612.08498","title":"Steerable CNNs","created":"2016-12-27","cited_by_count":305},{"id":"2002.00469","title":"WeatherBench: A benchmark dataset for data-driven weather forecasting","created":"2020-02-02","cited_by_count":275},{"id":"2005.03180","title":"Model Reduction and Neural Networks for Parametric PDEs","created":"2020-05-07","cited_by_count":258},{"id":"2109.13459","title":"Multiwavelet-based Operator Learning for Differential Equations","created":"2021-09-28","cited_by_count":234},{"id":"1707.09571","title":"Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics","created":"2017-07-30","cited_by_count":214},{"id":"1907.04490","title":"Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning","created":"2019-07-10","cited_by_count":212},{"id":"1812.04426","title":"PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network","created":"2018-11-30","cited_by_count":210},{"id":"2007.00016","title":"Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers","created":"2020-06-30","cited_by_count":205},{"id":"2104.12225","title":"DC3: A learning method for optimization with hard constraints","created":"2021-04-25","cited_by_count":184},{"id":"1612.08544","title":"Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data","created":"2016-12-27","cited_by_count":171},{"id":"2003.04919","title":"Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems","created":"2020-03-10","cited_by_count":164},{"id":"2006.04439","title":"Liquid Time-constant Networks","created":"2020-06-08","cited_by_count":161},{"id":"1806.04731","title":"Deep learning to represent sub-grid processes in climate models","created":"2018-06-12","cited_by_count":156},{"id":"1710.11431","title":"Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling","created":"2017-10-31","cited_by_count":131},{"id":"2010.04456","title":"Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting","created":"2020-10-09","cited_by_count":95},{"id":"1703.03126","title":"DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution","created":"2017-03-09","cited_by_count":82},{"id":"2001.11086","title":"Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles","created":"2020-01-28","cited_by_count":59},{"id":"1810.13075","title":"Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles","created":"2018-10-31","cited_by_count":55},{"id":"1711.04708","title":"Machine Learning for the Geosciences: Challenges and Opportunities","created":"2017-11-13","cited_by_count":53},{"id":"1912.01752","title":"Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability","created":"2019-12-04","cited_by_count":44},{"id":"1911.09145","title":"DPM: A deep learning PDE augmentation method (with application to large-eddy simulation)","created":"2019-11-20","cited_by_count":41},{"id":"1810.09675","title":"SwitchNet: a neural network model for forward and inverse scattering problems","created":"2018-10-23","cited_by_count":35},{"id":"1905.06841","title":"Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems","created":"2019-05-13","cited_by_count":33},{"id":"1811.06146","title":"Real-time Power System State Estimation and Forecasting via Deep Neural Networks","created":"2018-11-15","cited_by_count":32},{"id":"1906.06622","title":"Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling","created":"2019-06-15","cited_by_count":28},{"id":"2008.04679","title":"ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows","created":"2020-08-11","cited_by_count":25},{"id":"2010.00072","title":"Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations","created":"2020-09-30","cited_by_count":24},{"id":"2112.08440","title":"Climate-Invariant Machine Learning","created":"2021-12-14","cited_by_count":24},{"id":"1806.07439","title":"HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems","created":"2018-06-19","cited_by_count":21},{"id":"2001.09292","title":"The role of surrogate models in the development of digital twins of dynamic systems","created":"2020-01-25","cited_by_count":21},{"id":"2111.14671","title":"ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models","created":"2021-11-29","cited_by_count":21},{"id":"1709.00939","title":"DR-RNN: A deep residual recurrent neural network for model reduction","created":"2017-09-04","cited_by_count":20},{"id":"1807.05207","title":"Parametric generation of conditional geological realizations using generative neural networks","created":"2018-07-13","cited_by_count":19},{"id":"2002.11167","title":"Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning","created":"2020-02-25","cited_by_count":17},{"id":"2104.05089","title":"The World as a Graph: Improving El Ni\\~no Forecasts with Graph Neural Networks","created":"2021-04-11","cited_by_count":17},{"id":"2105.11521","title":"Deep neural network enabled corrective source term approach to hybrid analysis and modeling","created":"2021-05-24","cited_by_count":16},{"id":"2109.08770","title":"WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data","created":"2021-09-17","cited_by_count":11},{"id":"2106.00026","title":"Machine-Learning Non-Conservative Dynamics for New-Physics Detection","created":"2021-05-31","cited_by_count":10},{"id":"2105.02939","title":"PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling","created":"2021-05-05","cited_by_count":9},{"id":"1911.06671","title":"Enforcing Deterministic Constraints on Generative Adversarial Networks for Emulating Physical Systems","created":"2019-11-15","cited_by_count":7}],"next_cursor":null}