A review of physics-informed machine learning in fluid mechanics
Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …
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 …
deep learning (DL) has opened opportunities for fluid dynamics and its applications in …
Artificial intelligence for science in quantum, atomistic, and continuum systems
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 …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Geometric clifford algebra networks
Abstract We propose Geometric Clifford Algebra Networks (GCANs) for modeling dynamical
systems. GCANs are based on symmetry group transformations using geometric (Clifford) …
systems. GCANs are based on symmetry group transformations using geometric (Clifford) …
Group equivariant fourier neural operators for partial differential equations
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 …
(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
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 …
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 …
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 …
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
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 …
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
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 …
environments, enhancing the comfort, health, energy efficiency, and safety of indoor and …