CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers
K Hasegawa, K Fukami, T Murata… - Fluid Dynamics …, 2020 - iopscience.iop.org
We investigate the capability of machine learning (ML) based reduced order model (ML-
ROM) for two-dimensional unsteady flows around a circular cylinder at different Reynolds …
ROM) for two-dimensional unsteady flows around a circular cylinder at different Reynolds …
Exploring temporal dynamics of river discharge using univariate long short-term memory (LSTM) recurrent neural network at East Branch of Delaware River
River flow prediction is a pivotal task in the field of water resource management during the
era of rapid climate change. The highly dynamic and evolving nature of the climatic …
era of rapid climate change. The highly dynamic and evolving nature of the climatic …
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
We consider the use of probabilistic neural networks for fluid flow surrogate modeling and
data recovery. This framework is constructed by assuming that the target variables are …
data recovery. This framework is constructed by assuming that the target variables are …
Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution
Most modeling approaches lie in either of the two categories: physics‐based or data‐driven.
Recently, a third approach which is a combination of these deterministic and statistical …
Recently, a third approach which is a combination of these deterministic and statistical …
[HTML][HTML] Model fusion with physics-guided machine learning: Projection-based reduced-order modeling
The unprecedented amount of data generated from experiments, field observations, and
large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the …
large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the …
Accelerating multiscale electronic stopping power predictions with time-dependent density functional theory and machine learning
Knowing the rate at which particle radiation releases energy in a material, the “stopping
power,” is key to designing nuclear reactors, medical treatments, semiconductor and …
power,” is key to designing nuclear reactors, medical treatments, semiconductor and …
Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels
Modelling geophysical processes as low-dimensional dynamical systems and regressing
their vector field from data is a promising approach for learning emulators of such systems …
their vector field from data is a promising approach for learning emulators of such systems …
A deep learning framework for reconstructing experimental missing flow field of hydrofoil
Hydrofoils play a crucial role in enhancing the efficiency of fluid machinery designed for
ocean environments, reducing lift-induced drag and contributing to improved overall …
ocean environments, reducing lift-induced drag and contributing to improved overall …
emodarts: Joint optimisation of cnn & sequential neural network architectures for superior speech emotion recognition
Speech Emotion Recognition (SER) is crucial for enabling computers to understand the
emotions conveyed in human communication. With recent advancements in Deep Learning …
emotions conveyed in human communication. With recent advancements in Deep Learning …