English
Related papers

Related papers: Data reconstruction for complex flows using AI: re…

200 papers

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to…

Fluid Dynamics · Physics 2020-02-19 Steven Brunton , Bernd Noack , Petros Koumoutsakos

The fluid dynamics community has increasingly adopted machine learning to analyze, model, predict, and control a wide range of flows. These methods offer powerful computational capabilities for regression, compression, and optimization. In…

Fluid Dynamics · Physics 2025-08-26 Kunihiko Taira , Georgios Rigas , Kai Fukami

Big data and machine learning are driving comprehensive economic and social transformations and are rapidly re-shaping the toolbox and the methodologies of applied scientists. Machine learning tools are designed to learn functions from data…

Fluid Dynamics · Physics 2024-04-16 M. A. Mendez , J. Dominique , M. Fiore , F. Pino , P. Sperotto , J. Van den Berghe

Machine learning (ML) provides a broad spectrum of tools and architectures that enable the transformation of data from simulations and experiments into useful and explainable science, thereby augmenting domain knowledge. Furthermore,…

Plasma Physics · Physics 2024-09-05 Farbod Faraji , Maryam Reza

The renewed interest from the scientific community in machine learning (ML) is opening many new areas of research. Here we focus on how novel trends in ML are providing opportunities to improve the field of computational fluid dynamics…

Fluid Dynamics · Physics 2023-04-06 Ricardo Vinuesa , Steve Brunton

The field of machine learning has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the original big-data disciplines. This perspective will highlight…

Fluid Dynamics · Physics 2023-03-30 Ricardo Vinuesa , Steven L. Brunton , Beverley J. McKeon

The current revolution in the field of machine learning (ML) is leading to many interesting developments in a wide range of areas, including fluid mechanics. Here we review recent and emerging possibilities in the context of predictions,…

Fluid Dynamics · Physics 2023-10-09 Ricardo Vinuesa

This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques. We begin by introducing fundamental concepts, traditional methods, and benchmark datasets, then…

Unraveling the connections between microscopic structure, emergent physical properties, and slow dynamics has long been a challenge when studying the glass transition. The absence of clear visible structural order in amorphous…

The application of Machine Learning (ML) to hydrologic modeling is fledgling. Its applicability to capture the dependencies on watersheds to forecast better within a short period is fascinating. One of the key reasons to adopt ML algorithms…

Machine Learning · Computer Science 2025-10-14 Supath Dhital

For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for…

Machine Learning · Computer Science 2023-09-07 Hanxun Jin , Enrui Zhang , Horacio D. Espinosa

Flow map learning (FML), in conjunction with deep neural networks (DNNs), has shown promises for data driven modeling of unknown dynamical systems. A remarkable feature of FML is that it is capable of producing accurate predictive models…

Machine Learning · Computer Science 2023-07-21 Victor Churchill , Dongbin Xiu

Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous due to the high dimensionality of the solution space.…

Machine Learning · Computer Science 2023-03-23 Phong C. H. Nguyen , Joseph B. Choi , H. S. Udaykumar , Stephen Baek

The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to…

Machine Learning · Computer Science 2021-01-12 MohammadNoor Injadat , Abdallah Moubayed , Ali Bou Nassif , Abdallah Shami

The applications and impact of high fidelity simulation of fluid flows are far-reaching. They include settling some long-standing and fundamental questions in turbulence. However, the computational resources required for such efforts are…

Quantum Physics · Physics 2025-03-25 Sachin S. Bharadwaj , Katepalli R. Sreenivasan

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning revolution and have already been profoundly impacted by the…

Chemical Physics · Physics 2019-11-11 Frank Noé , Alexandre Tkatchenko , Klaus-Robert Müller , Cecilia Clementi

Building effective machine learning (ML) workflows to address complex tasks is a primary focus of the Automatic ML (AutoML) community and a critical step toward achieving artificial general intelligence (AGI). Recently, the integration of…

Machine Learning · Computer Science 2024-12-30 Yang Gu , Hengyu You , Jian Cao , Muran Yu , Haoran Fan , Shiyou Qian

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life,…

Machine Learning · Computer Science 2020-08-10 Amir Mosavi , Pinar Ozturk , Kwok-wing Chau

In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large databases and increased computational power, today's ML…

Artificial Intelligence · Computer Science 2019-09-27 Wojciech Samek , Klaus-Robert Müller

This work presents a review of the current state of research in data-driven turbulence closure modeling. It offers a perspective on the challenges and open issues, but also on the advantages and promises of machine learning methods applied…

Computational Engineering, Finance, and Science · Computer Science 2022-12-21 Andrea Beck , Marius Kurz
‹ Prev 1 2 3 10 Next ›