Zobrazeno 1 - 10
of 361
pro vyhledávání: '"Tartakovsky, Alexandre"'
We propose an approximate Bayesian method for quantifying the total uncertainty in inverse PDE solutions obtained with machine learning surrogate models, including operator learning models. The proposed method accounts for uncertainty in the observat
Externí odkaz:
http://arxiv.org/abs/2408.11145
We propose a randomized physics-informed neural network (PINN) or rPINN method for uncertainty quantification in inverse partial differential equation (PDE) problems with noisy data. This method is used to quantify uncertainty in the inverse PDE PINN
Externí odkaz:
http://arxiv.org/abs/2407.04617
We propose a physics-informed machine learning method for uncertainty quantification in high-dimensional inverse problems. In this method, the states and parameters of partial differential equations (PDEs) are approximated with truncated conditional
Externí odkaz:
http://arxiv.org/abs/2312.06177
We propose a methodology for improving the accuracy of surrogate models of the observable response of physical systems as a function of the systems' spatially heterogeneous parameter fields with applications to uncertainty quantification and paramete
Externí odkaz:
http://arxiv.org/abs/2307.02572
We present a model inversion algorithm, CKLEMAP, for data assimilation and parameter estimation in partial differential equation models of physical systems with spatially heterogeneous parameter fields. These fields are approximated using low-dimensi
Externí odkaz:
http://arxiv.org/abs/2301.11279
Autor:
Shen, Chaopeng, Appling, Alison P., Gentine, Pierre, Bandai, Toshiyuki, Gupta, Hoshin, Tartakovsky, Alexandre, Baity-Jesi, Marco, Fenicia, Fabrizio, Kifer, Daniel, Li, Li, Liu, Xiaofeng, Ren, Wei, Zheng, Yi, Harman, Ciaran J., Clark, Martyn, Farthing, Matthew, Feng, Dapeng, Kumar, Praveen, Aboelyazeed, Doaa, Rahmani, Farshid, Beck, Hylke E., Bindas, Tadd, Dwivedi, Dipankar, Fang, Kuai, Höge, Marvin, Rackauckas, Chris, Roy, Tirthankar, Xu, Chonggang, Mohanty, Binayak, Lawson, Kathryn
Publikováno v:
Nat Rev Earth Environ 4, 552-567 (2023)
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and
Externí odkaz:
http://arxiv.org/abs/2301.04027
In this paper, we develop a physics-informed neural network (PINN) model for parabolic problems with a sharply perturbed initial condition. As an example of a parabolic problem, we consider the advection-dispersion equation (ADE) with a point (Gaussi
Externí odkaz:
http://arxiv.org/abs/2208.08635
Numerical modeling and simulation have become indispensable tools for advancing a comprehensive understanding of the underlying mechanisms and cost-effective process optimization and control of flow batteries. In this study, we propose an enhanced ve
Externí odkaz:
http://arxiv.org/abs/2203.01985
Autor:
D'Elia, Marta, Deng, Hang, Fraces, Cedric, Garikipati, Krishna, Graham-Brady, Lori, Howard, Amanda, Karniadakis, George, Keshavarzzadeh, Vahid, Kirby, Robert M., Kutz, Nathan, Li, Chunhui, Liu, Xing, Lu, Hannah, Newell, Pania, O'Malley, Daniel, Prodanovic, Masa, Srinivasan, Gowri, Tartakovsky, Alexandre, Tartakovsky, Daniel M., Tchelepi, Hamdi, Vazic, Bozo, Viswanathan, Hari, Yoon, Hongkyu, Zarzycki, Piotr
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learn
Externí odkaz:
http://arxiv.org/abs/2202.04137
Autor:
Jamil, Ahsan, Rucker, Dale F., Lu, Dan, Brooks, Scott C., Tartakovsky, Alexandre M., Cao, Huiping, Carroll, Kenneth C.
Publikováno v:
In Journal of Applied Geophysics October 2024 229