Precise Simulation is proud to announce the release of FEATool Multiphysics™ version 1.18, a step forward in the_state-of-the-art_ in Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) multi-solver simulation software.
The latest release introduces a completely redesigned Graphical User Interface (GUI) making the toolbox more user friendly and_easy-to-use_ than ever before. FEATool Multiphysics also features full support and integration with MATLAB and related toolboxes (such as for Optimization, Control Systems, and_Machine Learning_), and advanced features tailored for the evolving needs of engineers and researchers in industry and academia.
New Standard for FEA, CFD & CAE AI/ML Simulation Workflows
One of the stand-out features of the FEATool toolbox is the Multiphysics Application Programming Interface (API), with one-click export functionality and automatic conversion of simulation models to MATLAB and Python script models. This enables users to quickly define and set up simulation models in a fully integrated and easy-to-use GUI, and later export, modify, and programmatically run them automatically for large scale parametric studies, and data generation and collection for Physics-Informed Neural Network (PINN), Machine Learning (ML) and artificial intelligence (AI) type simulation models.
An example of using FEATool Multiphysics to derive reference data as well as validation for machine learning CFD models can be found in several works on Deep Learning (DL) CFD methodology for flow prediction using with AI and machine learning by Prof. Thi-Thu-Huong Le and coworkers at the at the Blockchain Platform Research Center of the Pusan National University (PNU) in Korea.
Another example, here by Guodong Sa and coworkers, developed a Digital Twin (DT) framework for visualization and design of smart kitchens to facilitate improved kitchen and usability design. In the highlighted work, FEATool Multiphysics was used as a platform for programming and controlling simulation mesh and state-of-the-art CFD flow solvers such as OpenFOAM, and to script, automate, and programmatically generate thousands of sets of simulation data for training the digital twin framework.
And in the medical field, Prof. Zhang H. and coworkers have recently made use of FEATool Multiphysics to develop a method of denoising and improving vascular blood flow imaging for medical diagnosis using a Physics-Informed Neural Network (PINN). The data to train the PINN on was automatically generated by simulating the Navier-Stokes equations for different flow conditions and geometries. See their preprint on Fluid Dynamics and Domain Reconstruction from Noisy Flow Images Using Physics-Informed Neural Networks and Quasi-Conformal Mapping for more information.
For more detailed information on the new features and improvements, and to download the toolbox, please visit:
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