Výsledky vyhledávání - "RILEY, A. J."

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Towards Informatics-Driven Design of Nuclear Waste Forms

Autor: Hegde, Vinay I., Peterson, Miroslava, Allec, Sarah I., Lu, Xiaonan, Mahadevan, Thiruvillamalai, Nguyen, Thanh, Kalahe, Jayani, Oshiro, Jared, Seffens, Robert J., Nickerson, Ethan K., Du, Jincheng, Riley, Brian J., Vienna, John D., Saal, James E.

Informatics-driven approaches, such as machine learning and sequential experimental design, have shown the potential to drastically impact next-generation materials discovery and design. In this perspective, we present a few guiding principles for ap

Externí odkaz: http://arxiv.org/abs/2405.09897

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Machine-Learned Closure of URANS for Stably Stratified Turbulence: Connecting Physical Timescales & Data Hyperparameters of Deep Time-Series Models

Autor: Meena, Muralikrishnan Gopalakrishnan, Liousas, Demetri, Simin, Andrew D., Kashi, Aditya, Brewer, Wesley H., Riley, James J., Kops, Stephen M. de Bruyn

We develop time-series machine learning (ML) methods for closure modeling of the Unsteady Reynolds Averaged Navier Stokes (URANS) equations applied to stably stratified turbulence (SST). SST is strongly affected by fine balances between forces and be

Externí odkaz: http://arxiv.org/abs/2404.16141

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Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability

Autor: Allec, Sarah I., Lu, Xiaonan, Cassar, Daniel R., Nguyen, Xuan T., Hegde, Vinay I., Mahadevan, Thiruvillamalai, Peterson, Miroslava, Du, Jincheng, Riley, Brian J., Vienna, John D., Saal, James E.

Glasses form the basis of many modern applications and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain

Externí odkaz: http://arxiv.org/abs/2403.10682

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