Ben Moseley

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All published works

Action	Title	Year	Authors
+ PDF Chat	Modern, Efficient, and Differentiable Transport Equation Models using JAX: Applications to Population Balance Equations	2024	Mohammed Alsubeihi Arthur Jessop Ben Moseley Cláudio P. Fonte Ashwin Kumar Rajagopalan
+ PDF Chat	History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs)	2024	Jassem Abbasi Ben Moseley Takeshi Kurotori Ameya D. Jagtap Anthony R. Kovscek Aksel Hiorth Pål Østebø Andersen
+ PDF Chat	ELM-FBPINN: efficient finite-basis physics-informed neural networks	2024	S. Anderson Victorita Dolean Ben Moseley Jennifer Pestana
+	Multilevel domain decomposition-based architectures for physics-informed neural networks	2024	Victorita Dolean Alexander Heinlein Siddhartha Mishra Ben Moseley
+ PDF Chat	Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations	2023	Ben Moseley Andrew Markham Tarje Nissen‐Meyer
+	Multilevel domain decomposition-based architectures for physics-informed neural networks	2023	Victorita Dolean Alexander Heinlein Siddhartha Mishra Ben Moseley
+	Finite basis physics-informed neural networks as a Schwarz domain decomposition method	2022	Victorita Dolean Alexander Heinlein Siddhartha Mishra Ben Moseley
+	Solving the wave equation with physics-informed deep learning	2020	Ben Moseley Andrew Markham Tarje Nissen‐Meyer
+	Rk-means: Fast Clustering for Relational Data	2019	Ryan R. Curtin Ben Moseley Hung Q. Ngo XuanLong Nguyen Dan Olteanu Maximilian Schleich
+	On Coresets for Regularized Loss Minimization	2019	Ryan R. Curtin Sungjin Im Ben Moseley Kirk Pruhs Alireza Samadian

Common Coauthors

Coauthor	Papers Together
Victorita Dolean	4
Alexander Heinlein	3
Siddhartha Mishra	3
Ryan R. Curtin	2
Andrew Markham	2
Tarje Nissen‐Meyer	2
Kirk Pruhs	1
Cláudio P. Fonte	1
Takeshi Kurotori	1
Pål Østebø Andersen	1
Ameya D. Jagtap	1
Hung Q. Ngo	1
Jennifer Pestana	1
Alireza Samadian	1
Jassem Abbasi	1
Anthony R. Kovscek	1
S. Anderson	1
XuanLong Nguyen	1
Sungjin Im	1
Mohammed Alsubeihi	1
Ashwin Kumar Rajagopalan	1
Maximilian Schleich	1
Aksel Hiorth	1
Dan Olteanu	1
Arthur Jessop	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data	2019	Luning Sun Han Gao Shaowu Pan Jianxun Wang	3
+	The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies	2019	Ronen Basri David Jacobs Yoni Kasten Shira Kritchman	3
+ PDF Chat	Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks	2020	Zhi-Qin John Xu Zhi-Qin John Xu Yaoyu Zhang Yaoyu Zhang T. Luo Yanyang Xiao Zheng Ma Zheng	3
+	Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data	2019	Yinhao Zhu Nicholas Zabaras Phaedon‐Stelios Koutsourelakis Paris Perdikaris	3
+	PyTorch: An Imperative Style, High-Performance Deep Learning Library	2019	Adam Paszke Sam Gross Francisco Massa Adam Lerer James T. Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga	3
+	When and why PINNs fail to train: A neural tangent kernel perspective	2021	Sifan Wang Xinling Yu Paris Perdikaris	3
+	Towards Understanding the Spectral Bias of Deep Learning	2021	Yuan Cao Zhiying Fang Yue Wu Ding‐Xuan Zhou Quanquan Gu	3
+ PDF Chat	Learning the solution operator of parametric partial differential equations with physics-informed DeepONets	2021	Sifan Wang Hanwen Wang Paris Perdikaris	3
+	On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks	2021	Sifan Wang Hanwen Wang Paris Perdikaris	3
+ PDF Chat	Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations	2021	Suchuan Dong Zongwei Li	3
+ PDF Chat	Distributed learning machines for solving forward and inverse problems in partial differential equations	2020	Vikas Dwivedi Nishant Parashar B. Srinivasan	3
+	Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains	2020	Matthew Tancik Pratul P. Srinivasan Ben Mildenhall Sara Fridovich-Keil Nithin Raghavan Utkarsh Singhal Ravi Ramamoorthi Jonathan T. Barron Ren Ng	3
+	B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data	2020	Liu Yang Xuhui Meng George Em Karniadakis	3
+ PDF Chat	D3M: A Deep Domain Decomposition Method for Partial Differential Equations	2019	Ke Li Kejun Tang Tianfan Wu Qifeng Liao	3
+ PDF Chat	Nonlinear Preconditioning: How to Use a Nonlinear Schwarz Method to Precondition Newton's Method	2016	Victorita Dolean Martin J. Gander Walid Kheriji Félix Kwok Roland Masson	2
+ PDF Chat	On the limited memory BFGS method for large scale optimization	1989	Cheng‐Di Dong Jorge Nocedal	2
+	A Restricted Additive Schwarz Preconditioner for General Sparse Linear Systems	1999	Xiao‐Chuan Cai Marcus Sarkis	2
+	TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems	2016	Martı́n Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Gregory S. Corrado Andy Davis Jay B. Dean Matthieu Devin	2
+	On the Spectral Bias of Neural Networks	2018	Nasim Rahaman Aristide Baratin Devansh Arpit Felix Draxler Min Lin Fred A. Hamprecht Yoshua Bengio Aaron Courville	2
+ PDF Chat	Solving inverse problems using data-driven models	2019	Simon Arridge Peter Maaß Ozan Öktem Carola‐Bibiane Schönlieb	2
+	NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations	2020	Xiaowei Jin Shengze Cai Hui Li George Em Karniadakis	2
+	Solving the wave equation with physics-informed deep learning	2020	Ben Moseley Andrew Markham Tarje Nissen‐Meyer	2
+	Physics informed neural networks for simulating radiative transfer	2021	Siddhartha Mishra Roberto Molinaro	2
+	Physics-informed neural networks for inverse problems in nano-optics and metamaterials	2020	Yuyao Chen Lu Lu George Em Karniadakis Luca Dal Negro	2
+	Physics-informed learning of governing equations from scarce data	2020	Zhao Chen Yang Liu Hao Sun	2
+ PDF Chat	Large-Scale Neural Solvers for Partial Differential Equations	2020	Patrick Stiller Friedrich Bethke Maximilian Böhme Richard Pausch Sunna Torge Alexander Debus Jan Vorberger Michael Bußmann Nico Hoffmann	2
+ PDF Chat	Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations	2023	Ben Moseley Andrew Markham Tarje Nissen‐Meyer	2
+ PDF Chat	Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems	2022	Jared Willard Xiaowei Jia Shaoming Xu Michael Steinbach Vipin Kumar	2
+ PDF Chat	Physics-informed learning of governing equations from scarce data	2021	Zhao Chen Yang Liu Hao Sun	2
+	A coarse space acceleration of deep-DDM	2021	Valentin Mercier Serge Gratton Pierre Boudier	2
+	Finite basis physics-informed neural networks as a Schwarz domain decomposition method	2022	Victorita Dolean Alexander Heinlein Siddhartha Mishra Ben Moseley	2
+ PDF Chat	Self-adaptive physics-informed neural networks	2022	Levi D. McClenny Ulisses Braga-Neto	2
+	Towards Understanding the Spectral Bias of Deep Learning	2019	Yuan Cao Zhiying Fang Yue Wu Ding‐Xuan Zhou Quanquan Gu	1
+	Understanding and mitigating gradient pathologies in physics-informed neural networks	2020	Sifan Wang Yujun Teng Paris Perdikaris	1
+	Finding groups in data: an introduction to cluster analysis	1991		1
+	Up to two billion times acceleration of scientific simulations with deep neural architecture search	2020	Muhammad Kasim Duncan Watson‐Parris Lucia Deaconu Sophy Oliver Peter Hatfield D. H. Froula Giovanni Gregori M. J. Jarvis Samar Khatiwala Jun Korenaga	1
+	Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations	2020	Liu Yang Dongkun Zhang George Em Karniadakis	1
+ PDF Chat	NeuroDiffEq: A Python package for solving differential equations with neural networks	2020	Feiyu Chen David Sondak Pavlos Protopapas Marios Mattheakis Shuheng Liu Devansh Agarwal Marco Di Giovanni	1
+ PDF Chat	Regularized k-means clustering of high-dimensional data and its asymptotic consistency	2012	Wei Sun Junhui Wang Yixin Fang	1
+ PDF Chat	A unified deep artificial neural network approach to partial differential equations in complex geometries	2018	Jens Berg Kaj Nyström	1
+ PDF Chat	hp-VPINNs: Variational physics-informed neural networks with domain decomposition	2020	Ehsan Kharazmi Zhongqiang Zhang George Em Karniadakis	1
+ PDF Chat	DeepXDE: A Deep Learning Library for Solving Differential Equations	2021	Lu Lu Xuhui Meng Zhiping Mao George Em Karniadakis	1
+	Machine learning and domain decomposition methods -- a survey	2023	Axel Klawonn Martin Lanser Janine Weber	1
+ PDF Chat	Constraint Solving via Fractional Edge Covers	2014	Martin Grohe Dániel Marx	1
+	Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs	2020	Siddhartha Mishra Roberto Molinaro	1
+ PDF Chat	Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains	2020	Ziqi Liu Wei Cai Zhi-Qin John Xu Zhi-Qin John Xu	1
+ PDF Chat	Juggling Functions Inside a Database	2017	Mahmoud Abo Khamis Hung Q. Ngo Atri Rudra	1
+	Building high accuracy emulators for scientific simulations with deep neural architecture search	2021	Muhammad Kasim Duncan Watson‐Parris Lucia Deaconu Sophy Oliver Peter Hatfield D. H. Froula Giovanni Gregori M. J. Jarvis Samar Khatiwala Jun Korenaga	1
+	First International Symposium on Domain Decomposition Methods for Partial Differential Equations.	1989	J. H. B. Roland Glowinski Gene H. Golub Gérard Meurant Jacques Périaux	1
+ PDF Chat	Artificial neural networks for solving ordinary and partial differential equations	1998	I.E. Lagaris Aristidis Likas D.I. Fotiadis	1