Zobrazeno 1 - 10
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pro vyhledávání: '"Lorsung, Cooper"'
Autor:
Lorsung, Cooper, Farimani, Amir Barati
Solving Partial Differential Equations (PDEs) is ubiquitous in science and engineering. Computational complexity and difficulty in writing numerical solvers has motivated the development of machine learning techniques to generate solutions quickly. M
Externí odkaz:
http://arxiv.org/abs/2410.01137
Publikováno v:
Transactions on Machine Learning Research, 2024
Pretraining for partial differential equation (PDE) modeling has recently shown promise in scaling neural operators across datasets to improve generalizability and performance. Despite these advances, our understanding of how pretraining affects neur
Externí odkaz:
http://arxiv.org/abs/2406.08473
Autor:
Lorsung, Cooper, Farimani, Amir Barati
Neural operators have recently grown in popularity as Partial Differential Equation (PDE) surrogate models. Learning solution functionals, rather than functions, has proven to be a powerful approach to calculate fast, accurate solutions to complex PD
Externí odkaz:
http://arxiv.org/abs/2401.16327
Solving Partial Differential Equations (PDEs) is the core of many fields of science and engineering. While classical approaches are often prohibitively slow, machine learning models often fail to incorporate complete system information. Over the past
Externí odkaz:
http://arxiv.org/abs/2305.08757
Modeling the ion concentration profile in nanochannel plays an important role in understanding the electrical double layer and electroosmotic flow. Due to the non-negligible surface interaction and the effect of discrete solvent molecules, molecular
Externí odkaz:
http://arxiv.org/abs/2304.04896
Autor:
Wheeler, William A., Pathak, Shivesh, Kleiner, Kevin, Yuan, Shunyue, Rodrigues, João N. B., Lorsung, Cooper, Krongchon, Kittithat, Chang, Yueqing, Zhou, Yiqing, Busemeyer, Brian, Williams, Kiel T., Muñoz, Alexander, Chow, Chun Yu, Wagner, Lucas K.
We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms in an accessible format, enabling algorithm
Externí odkaz:
http://arxiv.org/abs/2212.01482
Autor:
Lorsung, Cooper, Farimani, Amir Barati
Meshing is a critical, but user-intensive process necessary for stable and accurate simulations in computational fluid dynamics (CFD). Mesh generation is often a bottleneck in CFD pipelines. Adaptive meshing techniques allow the mesh to be updated au
Externí odkaz:
http://arxiv.org/abs/2212.01428
Autor:
Magar, Rishikesh, Wang, Yuyang, Lorsung, Cooper, Liang, Chen, Ramasubramanian, Hariharan, Li, Peiyuan, Farimani, Amir Barati
Machine learning (ML) has demonstrated the promise for accurate and efficient property prediction of molecules and crystalline materials. To develop highly accurate ML models for chemical structure property prediction, datasets with sufficient sample
Externí odkaz:
http://arxiv.org/abs/2111.15112
Autor:
Lorsung, Cooper
Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainty by learning features from the data and then performing Bayesian linear regression over these features. Despite their popularity, few works have focused on formall
Externí odkaz:
http://arxiv.org/abs/2106.13055
Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainties by learning features from the data and then performing Bayesian linear regression over these features. Despite their popularity, few works have focused on metho
Externí odkaz:
http://arxiv.org/abs/2006.11695