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Multi-fidelity modeling and calibration are data fusion tasks that ubiquitously arise in engineering design. In this paper, we introduce a novel approach based on latent-map Gaussian processes (LMGPs) that enables efficient and accurate…

Machine Learning · Statistics 2022-01-17 Nicholas Oune , Jonathan Tammer Eweis-Labolle , Ramin Bostanabad

Performance optimization is the art of continuous seeking a harmonious mapping between the application domain and hardware. Recent years have witnessed a surge of deep learning (DL) applications in industry. Conventional wisdom for…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-11-27 Guoping Long , Jun Yang , Wei Lin

We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. While previous fusion methods use an explicit scene representation like signed distance functions (SDFs), we propose a learned…

Computer Vision and Pattern Recognition · Computer Science 2021-06-09 Silvan Weder , Johannes L. Schönberger , Marc Pollefeys , Martin R. Oswald

Data driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning…

Machine Learning · Computer Science 2025-10-01 Erfan Hamdi , Emma Lejeune

A hybrid sharp-interface immersed-boundary/front-tracking (IB/FT) method is developed for interface-resolved simulation of evaporating droplets in incompressible multiphase flows. A one-field formulation is used to solve the flow, species…

Fluid Dynamics · Physics 2025-10-30 Faraz Salimnezhad , Hasret Turkeri , Iskender Gokalp , Metin Muradoglu

A new computational methodology, termed "PeleLM-FDF" is developed and utilized for high fidelity large eddy simulation (LES) of complex turbulent combustion systems. This methodology is constructed via a hybrid scheme combining the Eulerian…

Fluid Dynamics · Physics 2023-01-11 Aidyn Aitzhan , Shervin Sammak , Peyman Givi , Arash G. Nouri

Over the past few decades, tremendous progress has been made in the development of particle-based discrete simulation methods versus the conventional continuum-based methods. In particular, the lattice Boltzmann (LB) method has evolved from…

Computational Physics · Physics 2015-12-08 Qing Li , K. H. Luo , Q. J. Kang , Y. L. He , Q. Chen , Q. Liu

Thermal scene reconstruction holds great potential for various applications, such as analyzing building energy consumption and performing non-destructive infrastructure testing. However, existing methods typically require dense scene…

Computer Vision and Pattern Recognition · Computer Science 2024-12-02 Mariam Hassan , Florent Forest , Olga Fink , Malcolm Mielle

This work presents a multi-level modeling and design framework for weft knitted fabrics, beginning with a volumetric finite element analysis capturing their mechanical behavior from fundamental principles. Incorporating yarn-level data, it…

High stress occurs when 3D heterogeneous IC packages are subjected to thermal cycling at extreme temperatures. Stress mainly occurs at the interface between different materials. We investigate stress image using latent space representation…

Machine Learning · Computer Science 2026-02-26 Kart Leong Lim , Ji Lin

This work investigated the effects of aging heat treatments on the microstructure and, consequently, the quasi-static (tensile properties) and dynamic (impact toughness) mechanical behaviour of a 17-4 PH stainless steel produced by powder…

This work studies the influence of several compositional effects on thermal and reactive processes. First, the impact of using a fully compositional model in the context of thermal simulations is considered. Detailed phase behavior models…

Computational Physics · Physics 2021-11-03 Matthias A. Cremon , Margot G. Gerritsen

Data-fusion networks have shown significant promise for RGB-thermal scene parsing. However, the majority of existing studies have relied on symmetric duplex encoders for heterogeneous feature extraction and fusion, paying inadequate…

Computer Vision and Pattern Recognition · Computer Science 2026-01-07 Jiahang Li , Peng Yun , Yang Xu , Ye Zhang , Mingjian Sun , Qijun Chen , Ilin Alexander , Rui Fan

High-throughput experiments that use combinatorial samples with rapid measurements can be used to provide process-structure-property information at reduced time, cost, and effort. Developing these tools and methods is essential in additive…

The growing availability of computational resources has significantly increased the interest of the scientific community in performing complex multi-physics and multi-domain simulations. However, the generation of appropriate computational…

Numerical Analysis · Mathematics 2026-04-03 Daniele Moretto , Andrea Franceschini , Massimiliano Ferronato

With the growing demand of mineral consumption, the management of the mining waste is crucial. Cemented paste backfill (CPB) is one of the techniques developed by the mining industry to fill the voids generated by the excavation of…

Numerical Analysis · Mathematics 2024-02-29 Clément Berger , David Coulette , Paul Vigneaux

Immersed boundary-lattice Boltzmann method (IB-LBM) has been widely used for simulation of particle-laden flows recently. However, it was limited to small-scale simulations with no more than O(103) particles. Here, we expand IB-LBM for…

Computational Physics · Physics 2020-02-21 Maoqiang Jiang , Jing Li , Zhaohui Liu

Separated flow transition is a very popular phenomenon in gas turbines, especially low-pressure turbines (LPT). Low-fidelity simulations are often used for gas turbine design. However, they are unable to predict separated flow transition…

Fluid Dynamics · Physics 2024-09-13 Harshal D. Akolekar

The large number of possible structures of metal-organic frameworks (MOFs) and their limitless potential applications has motivated molecular modelers and researchers to develop methods and models to efficiently assess MOF performance. Some…

Materials Science · Physics 2021-10-04 Krishnendu Mukherjee , Alexander W. Dowling , Yamil Colón

Scientific Machine Learning is transforming traditional engineering industries by enhancing the efficiency of existing technologies and accelerating innovation, particularly in modeling chemical reactions. Despite recent advancements, the…

Machine Learning · Computer Science 2024-08-21 Imran Nasim , Joaõ Lucas de Sousa Almeida
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