Zobrazeno 1 - 10
of 274
pro vyhledávání: '"Doostan, Alireza"'
This work leverages laser vibrometry and the weak form of the sparse identification of nonlinear dynamics (WSINDy) for partial differential equations to learn macroscale governing equations from full-field experimental data. In the experiments, two b
Externí odkaz:
http://arxiv.org/abs/2409.20510
Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or accurate resolution of their solutions may be intractable. Instead, recently
Externí odkaz:
http://arxiv.org/abs/2406.02581
Traditional low-rank approximation is a powerful tool to compress the huge data matrices that arise in simulations of partial differential equations (PDE), but suffers from high computational cost and requires several passes over the PDE data. The co
Externí odkaz:
http://arxiv.org/abs/2405.16076
Autor:
Hassanaly, Malik, Weddle, Peter J., King, Ryan N., De, Subhayan, Doostan, Alireza, Randall, Corey R., Dufek, Eric J., Colclasure, Andrew M., Smith, Kandler
Publikováno v:
Journal of Energy Storage, Volume 98, Part B, 2024, 113104
Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be easily repeated for multiple cycles, multiple operating conditions, or mu
Externí odkaz:
http://arxiv.org/abs/2312.17336
Autor:
Hassanaly, Malik, Weddle, Peter J., King, Ryan N., De, Subhayan, Doostan, Alireza, Randall, Corey R., Dufek, Eric J., Colclasure, Andrew M., Smith, Kandler
Publikováno v:
Journal of Energy Storage, Volume 98, Part B, 2024, 113103
To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapidly. This study seeks to reduce the computational resources needed to d
Externí odkaz:
http://arxiv.org/abs/2312.17329
Autor:
Balin, Riccardo, Simini, Filippo, Simpson, Cooper, Shao, Andrew, Rigazzi, Alessandro, Ellis, Matthew, Becker, Stephen, Doostan, Alireza, Evans, John A., Jansen, Kenneth E.
Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. Additio
Externí odkaz:
http://arxiv.org/abs/2306.12900
Publikováno v:
Computer Methods in Applied Mechanics and Engineering (CMAME), Volume 421, 1 March 2024, 116793
Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary objective in model validation. However, achieving this goal entails balancing the need for computational efficiency with the requirement for numerical acc
Externí odkaz:
http://arxiv.org/abs/2305.16530
Autor:
Wentz, Jacqueline, Doostan, Alireza
Recent advances in the field of data-driven dynamics allow for the discovery of ODE systems using state measurements. One approach, known as Sparse Identification of Nonlinear Dynamics (SINDy), assumes the dynamics are sparse within a predetermined b
Externí odkaz:
http://arxiv.org/abs/2211.05918
Publikováno v:
Journal of Computational Physics, Volume 498, 1 February 2024
We present a new convolution layer for deep learning architectures which we call QuadConv -- an approximation to continuous convolution via quadrature. Our operator is developed explicitly for use on non-uniform, mesh-based data, and accomplishes thi
Externí odkaz:
http://arxiv.org/abs/2211.05151
Autor:
Cheng, Nuojin, Malik, Osman Asif, Xu, Yiming, Becker, Stephen, Doostan, Alireza, Narayan, Akil
Publikováno v:
SIAM/ASA Journal on Uncertainty Quantification, Vol. 12, Iss. 2 (2024)
Least squares regression is a ubiquitous tool for building emulators (a.k.a. surrogate models) of problems across science and engineering for purposes such as design space exploration and uncertainty quantification. When the regression data are gener
Externí odkaz:
http://arxiv.org/abs/2209.05705