Physics-guided, physics-informed, and physics-encoded neural networks in scientific computing
Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …
and deep learning to advance scientific computing in many fields, including fluid mechanics …
Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
SA Faroughi, NM Pawar… - Journal of …, 2024 - asmedigitalcollection.asme.org
Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …
learning and deep learning to push scientific computing forward in a range of disciplines …
Modelling the unsteady lift of a pitching NACA 0018 aerofoil using state-space neural networks
The development of simple, low-order and accurate unsteady aerodynamic models
represents a crucial challenge for the design optimisation and control of fluid dynamical …
represents a crucial challenge for the design optimisation and control of fluid dynamical …
Unsteady Aerodynamic Lift Force on a Pitching Wing: Experimental Measurement and Data Processing
This work discusses the experimental challenges and processing of unsteady experiments
for a pitching wing in the low-speed wind tunnel of the Vrije Universiteit Brussel. The setup …
for a pitching wing in the low-speed wind tunnel of the Vrije Universiteit Brussel. The setup …
Advanced vibration control strategies for Electro-Hydraulic testing systems focus on sinusoidal Swept-Frequency techniques
This paper presents an advanced investigation into vibration control strategies for electro-
hydraulic testing systems, with a specific emphasis on sinusoidal swept-frequency …
hydraulic testing systems, with a specific emphasis on sinusoidal swept-frequency …
[HTML][HTML] LPRM: A user-friendly iteration-free combined Local Polynomial and Rational Method toolbox for measurements of multiple input systems
PZ Csurcsia - Software Impacts, 2022 - Elsevier
This paper introduces a user-friendly estimation toolbox for (industrial) measurements of
(vibro-acoustic) systems with multiple inputs. The vibration testing methods are very …
(vibro-acoustic) systems with multiple inputs. The vibration testing methods are very …
Constructing nonlinear data-driven models from pitching wing experiments using multisine excitation signals
Accurate modelling of unsteady nonlinear aerodynamic loads is crucial for the effective
design and control of aerodynamic systems. Data-driven modelling approaches have …
design and control of aerodynamic systems. Data-driven modelling approaches have …
Modeling airfoil dynamic stall using State-Space Neural Networks
View Video Presentation: https://doi. org/10.2514/6.2023-1945. vid The present paper
investigates the effectiveness of artificial neural networks for the identification of nonlinear …
investigates the effectiveness of artificial neural networks for the identification of nonlinear …
Data-driven aerodynamic models for aeroelastic simulations
Multiple approaches are available for calculating the time-dependent aerodynamic loads of
thin, flexible structures subjected to airflow: analytical, semi-empirical, CFD-based, and …
thin, flexible structures subjected to airflow: analytical, semi-empirical, CFD-based, and …
[HTML][HTML] MUMI: Multisine for multiple input systems: A user-friendly excitation toolbox for physical systems
PZ Csurcsia - Software Impacts, 2022 - Elsevier
Scientists and engineers want accurate mathematical models of physical systems for
understanding, design, and control. To obtain accurate models, persistently exciting rich …
understanding, design, and control. To obtain accurate models, persistently exciting rich …