A review of physics-informed machine learning in fluid mechanics

P Sharma, WT Chung, B Akoush, M Ihme - Energies, 2023 - mdpi.com
Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …

Can artificial intelligence accelerate fluid mechanics research?

D Drikakis, F Sofos - Fluids, 2023 - mdpi.com
The significant growth of artificial intelligence (AI) methods in machine learning (ML) and
deep learning (DL) has opened opportunities for fluid dynamics and its applications in …

Artificial intelligence for science in quantum, atomistic, and continuum systems

X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie… - arXiv preprint arXiv …, 2023 - arxiv.org
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Geometric clifford algebra networks

D Ruhe, JK Gupta, S De Keninck… - International …, 2023 - proceedings.mlr.press
Abstract We propose Geometric Clifford Algebra Networks (GCANs) for modeling dynamical
systems. GCANs are based on symmetry group transformations using geometric (Clifford) …

Group equivariant fourier neural operators for partial differential equations

J Helwig, X Zhang, C Fu, J Kurtin… - arXiv preprint arXiv …, 2023 - arxiv.org
We consider solving partial differential equations (PDEs) with Fourier neural operators
(FNOs), which operate in the frequency domain. Since the laws of physics do not depend on …

Bubbleml: A multiphase multiphysics dataset and benchmarks for machine learning

SMS Hassan, A Feeney, A Dhruv… - Advances in …, 2024 - proceedings.neurips.cc
In the field of phase change phenomena, the lack of accessible and diverse datasets
suitable for machine learning (ML) training poses a significant challenge. Existing …

Deep learning reconstruction of pressure fluctuations in supersonic shock–boundary layer interaction

K Poulinakis, D Drikakis, IW Kokkinakis… - Physics of …, 2023 - pubs.aip.org
The long short-term memory deep-learning model is applied to supersonic shock–boundary
layer interaction flow. The study aims to show how near-wall pressure fluctuations can be …

[HTML][HTML] Differentiability in unrolled training of neural physics simulators on transient dynamics

B List, LW Chen, K Bali, N Thuerey - Computer Methods in Applied …, 2025 - Elsevier
Unrolling training trajectories over time strongly influences the inference accuracy of neural
network-augmented physics simulators. We analyze these effects by studying three variants …

Turbulence in focus: Benchmarking scaling behavior of 3d volumetric super-resolution with blastnet 2.0 data

WT Chung, B Akoush, P Sharma… - Advances in …, 2024 - proceedings.neurips.cc
Abstract Analysis of compressible turbulent flows is essential for applications related to
propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 …

[HTML][HTML] Machine Learning to speed up Computational Fluid Dynamics engineering simulations for built environments: A review

C Caron, P Lauret, A Bastide - Building and Environment, 2024 - Elsevier
Computational fluid dynamics (CFD) represents a valuable tool in the design process of built
environments, enhancing the comfort, health, energy efficiency, and safety of indoor and …