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Ameya D. Jagtap

worcester polytechnic institute

16H指数
57论文数
5.3K被引数
收录论文 21
发表时间
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Intelligent fluid flows: A survey of deep learning methods for turbulent flows, multiphase flows, and combustion智能流体流动：用于湍流、多相流和燃烧的深度学习方法综述
errNeurocomputing
IF6.5
err2026-06-04
err0
errOAAI
errSidharth S. Menon; Mahdi Lavari; Amelia Kokernak; Joel Mathew; Charulatha A. Jagtap; Jagannath Jayachandran; Aswin Gnanaskandan; Ameya D. Jagtap
err分享
err收藏
FEKAN: Feature-Enriched Kolmogorov-Arnold NetworksFEKAN：特征增强的柯尔莫哥洛夫-阿尔诺德网络
errComputer Methods in Applied Mechanics and Engineering
IF7.3
err2026-06-01
err0
PREAI
errMenon, Sidharth S.; Jagtap, Ameya D.
err分享
err收藏
An approximation theory perspective on machine learning从逼近理论视角看机器学习
errNeural Networks
IF6.3
err2026-03-13
err0
errOAAI
errHrushikesh N. Mhaskar; Efstratios Tsoukanis; Ameya D. Jagtap
err分享
err收藏
BubbleOKAN: A physics-informed interpretable neural operator for high-frequency bubble dynamicsBubbleOKAN：一种用于高频气泡动力学的高物理感知可解释神经网络算子
errComputer Methods in Applied Mechanics and Engineering
IF7.3
err2025-12-18
err0
errOAAI
errYunhao Zhang; Sidharth S. Menon; Lin Cheng; Aswin Gnanaskandan; Ameya D. Jagtap
err分享
err收藏
Anant-Net: Breaking the curse of dimensionality with scalable and interpretable neural surrogate for high-dimensional PDEsAnant-Net：使用可扩展且可解释的神经代理模型突破高维PDEs的维度灾难
errComputer Methods in Applied Mechanics and Engineering
IF7.3
err2025-09-17
err0
errOAAI
errSidharth S. Menon; Ameya D. Jagtap
err分享
err收藏
Challenges and advancements in modeling shock fronts with physics-informed neural networks: A review and benchmarking study基于物理信息神经网络模拟激波前缘的挑战与进展：一项综述与基准研究
errNeurocomputing
IF6.5
err2025-09-01
err0
PREAI
errJassem Abbasi; Ameya D. Jagtap; Ben Moseley; Aksel Hiorth; Pål Østebø Andersen
err分享
err收藏
Large language model-based evolutionary optimizer: Reasoning with elitism基于大型语言模型的进化优化器: 精英主义推理
errNEUROCOMPUTING
IF6.5
err2025-03-01
err0
PREAI
errBrahmachary, Shuvayan; Joshi, Subodh M.; Panda, Aniruddha; Koneripalli, Kaushik; Sagotra, Arun Kumar; Patel, Harshil; Sharma, Ankush; Jagtap, Ameya D.; Kalyanaraman, Kaushic
err分享
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RiemannONets: Interpretable neural operators for Riemann problems
errCOMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
IF7.3
err2024-06-01
err3
errOAAI
errPeyvan, Ahmad; Oommen, Vivek; Jagtap, Ameya D.; Karniadakis, George Em
err分享
err收藏
Deep smoothness weighted essentially non-oscillatory method for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators
errPHYSICS OF FLUIDS
IF4.3
err2024-03-04
err1
PREAI
errKossaczka, Tatiana; Jagtap, Ameya D.; Ehrhardt, Matthias
err分享
err收藏
Learning stiff chemical kinetics using extended deep neural operators
errCOMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
IF7.3
err2024-02-01
err17
errOAAI
errGoswami, Somdatta; Jagtap, Ameya D.; Babaee, Hessam; Susi, Bryan T.; Karniadakis, George Em
err分享
err收藏
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions用于物理信息神经网络 (pinn) 及其时间分解的因果扫描策略的统一可扩展框架
errJOURNAL OF COMPUTATIONAL PHYSICS
IF3.8
err2023-11-01
err29
errOAAI
errPenwarden, Michael; Jagtap, Ameya D.; Zhe, Shandian; Karniadakis, George Em; Kirby, Robert M.
err分享
err收藏
Physics-informed neural networks for inverse problems in supersonic flows用于超音速流中逆问题的物理通知神经网络
errJOURNAL OF COMPUTATIONAL PHYSICS
IF3.8
err2022-10-01
err152
errOAAI
errJagtap, Ameya D.; Mao, Zhiping; Adams, Nikolaus; Karniadakis, George Em
err分享
err收藏
WHEN DO EXTENDED PHYSICS-INFORMED NEURAL NETWORKS (XPINNS) IMPROVE GENERALIZATION?扩展的物理信息神经网络 (XPINNS) 何时提高泛化能力？
errSIAM JOURNAL ON SCIENTIFIC COMPUTING
IF2.6
err2022-09-27
err47
errOAAI
errHu, Zheyuan; Jagtap, Ameya D.; Karniadakis, George Em; Kawaguchi, Kenji
err分享
err收藏
A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems
errIEEE SIGNAL PROCESSING MAGAZINE
IF9.6
err2022-01-01
err63
errOAAI
errShukla, Khemraj; Jagtap, Ameya D.; Blackshire, James L.; Sparkman, Daniel; Karniadakis, George Em
err分享
err收藏
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions深度Kronecker神经网络: 具有自适应激活函数的神经网络的通用框架
errNEUROCOMPUTING
IF6.5
err2022-01-01
err110
errOAAI
errJagtap, Ameya D.; Shin, Yeonjong; Kawaguchi, Kenji; Karniadakis, George Em
err分享
err收藏
Parallel physics-informed neural networks via domain decomposition通过域分解的并行物理通知神经网络
errJOURNAL OF COMPUTATIONAL PHYSICS
IF3.8
err2021-12-01
err207
errOAAI
errShukla, Khemraj; Jagtap, Ameya D.; Karniadakis, George Em
err分享
err收藏
L1-type smoothness indicators based WENO scheme for nonlinear degenerate parabolic equations基于L1-type光滑性指标的非线性退化抛物方程WENO格式
errAPPLIED MATHEMATICS AND COMPUTATION
IF3.4
err2020-06-01
err15
PREAI
errRathan, Samala; Kumar, Rakesh; Jagtap, Ameya D.
err分享
err收藏
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems保守物理知识的神经网络在离散域上的守恒律: 正向和反向问题的应用
errCOMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
IF7.3
err2020-06-01
err543
errOAAI
errJagtap, Ameya D.; Kharazmi, Ehsan; Karniadakis, George Em
err分享
err收藏
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks自适应激活函数加速深度和物理信息神经网络的收敛
errJOURNAL OF COMPUTATIONAL PHYSICS
IF3.8
err2020-03-01
err611
errOAAI
errJagtap, Ameya D.; Kawaguchi, Kenji; Karniadakis, George Em
err分享
err收藏
Physics-informed neural networks for high-speed flows用于高速流动的物理通知神经网络
errCOMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
IF7.3
err2020-03-01
err682
errOAAI
errMao, Zhiping; Jagtap, Ameya D.; Karniadakis, George Em
err分享
err收藏

研究方向

暂时未获取到该数据
合作学者
合作期刊
G
George Em Karniadakis
H 指数: 130 · 论文数: 1.3K
N
Nikolaus A. Adams
H 指数: 65 · 论文数: 653
S
Suresh Menon
H 指数: 51 · 论文数: 493
R
Robert M. Kirby
H 指数: 43 · 论文数: 248
K
Kenji Kawaguchi
H 指数: 40 · 论文数: 455
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