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Data Science is currently a popular field of science attracting expertise from very diverse backgrounds. Current learning practices need to acknowledge this and adapt to it. This paper summarises some experiences relating to such learning…

General Literature · Computer Science 2018-07-11 Yehia Elkhatib

Flow matching has shown state-of-the-art performance in various generative tasks, ranging from image generation to decision-making, where generation under energy guidance (abbreviated as guidance in the following) is pivotal. However, the…

Machine Learning · Computer Science 2025-05-27 Ruiqi Feng , Chenglei Yu , Wenhao Deng , Peiyan Hu , Tailin Wu

We live in a world driven by data. The amount of it outgrows anyone's ability to oversee it or even observe its scope. Along with all the advances in the space of data management, there is still a significant lack of formalism and…

Databases · Computer Science 2020-02-03 Egor Pushkin

We demonstrate several techniques to encourage practical uses of neural networks for fluid flow estimation. In the present paper, three perspectives which are remaining challenges for applications of machine learning to fluid dynamics are…

Fluid Dynamics · Physics 2022-05-19 Masaki Morimoto , Kai Fukami , Kai Zhang , Koji Fukagata

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

Flood-related risks to people and property are expected to increase in the future due to environmental and demographic changes. It is important to quantify and effectively communicate flood hazards and exposure to inform the design and…

Applications · Statistics 2021-08-27 Sanjib Sharma , Michael Gomez , Klaus Keller , Robert Nicholas , Alfonso Mejia

Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the…

Forecasting production reliably and anticipating changes in the behavior of rock-fluid systems are the main challenges in petroleum reservoir engineering. This project proposes to deal with this problem through a data-driven approach and…

Machine Learning · Computer Science 2025-08-27 Mateus A. Fernandes , Michael M. Furlanetti , Eduardo Gildin , Marcio A. Sampaio

Climate science is critical for understanding both the causes and consequences of changes in global temperatures and has become imperative for decisive policy-making. However, climate science studies commonly require addressing complex…

Machine Learning · Computer Science 2020-09-23 Juan Carrillo , Daniel Garijo , Mark Crowley , Rober Carrillo , Yolanda Gil , Katherine Borda

Information flow analysis has largely ignored the setting where the analyst has neither control over nor a complete model of the analyzed system. We formalize such limited information flow analyses and study an instance of it: detecting the…

Cryptography and Security · Computer Science 2014-05-13 Michael Carl Tschantz , Amit Datta , Anupam Datta , Jeannette M. Wing

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long…

Computer Vision and Pattern Recognition · Computer Science 2020-05-14 Zifeng Guo , Joao P. Leitao , Nuno E. Simoes , Vahid Moosavi

We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold. Combining aspects of normalizing flows, GANs,…

Machine Learning · Statistics 2020-11-16 Johann Brehmer , Kyle Cranmer

The objective of this three-part work is to formulate and rigorously analyse a number of reduced mathematical models that are nevertheless capable of describing the hydrology at the scale of a river basin (i.e. catchment). Coupled surface…

Fluid Dynamics · Physics 2023-12-29 Piotr Morawiecki , Philippe H. Trinh

We propose a physics-constrained machine learning method-based on reservoir computing- to time-accurately predict extreme events and long-term velocity statistics in a model of turbulent shear flow. The method leverages the strengths of two…

Fluid Dynamics · Physics 2021-04-14 Nguyen Anh Khoa Doan , Wolfgang Polifke , Luca Magri

Current modeling approaches for hydrological modeling often rely on either physics-based or data-science methods, including Machine Learning (ML) algorithms. While physics-based models tend to rigid structure resulting in unrealistic…

Machine Learning · Statistics 2021-04-23 Pravin Bhasme , Jenil Vagadiya , Udit Bhatia

Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e. assumptions on the probability…

Applications · Statistics 2021-12-09 Hristos Tyralis , Georgia Papacharalampous

With the possible exception of gambling, meteorology, particularly precipitation forecasting, may be the area with which the general public is most familiar with probabilistic assessments of uncertainty. Despite the heavy use of stochastic…

Applications · Statistics 2009-01-23 Montserrat Fuentes , Peter Guttorp , Michael L. Stein

Turbulence modeling is a critical component in numerical simulations of industrial flows based on Reynolds-averaged Navier-Stokes (RANS) equations. However, after decades of efforts in the turbulence modeling community, universally…

Fluid Dynamics · Physics 2017-03-22 Jian-Xun Wang , Jin-Long Wu , Heng Xiao

Recent advances in data science, machine learning, and artificial intelligence, such as the emergence of large language models, are leading to an increasing demand for data that can be processed by such models. While data sources are…

Machine Learning · Computer Science 2023-09-13 Paul Bilokon , Oleksandr Bilokon , Saeed Amen

In this work we explore the advantages of end-to-end learning of multilayer maps offered by feed forward neural-networks (FFNN) for learning and predicting dynamics from transient fluid flow data.While machine learning in general depends on…

Computational Physics · Physics 2020-10-28 Shivakanth Chary Puligilla , Balaji Jayaraman