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ORCIDAdvanced Modeling and Simulation in Engineering Sciences, Volume 9
Volume 9, Number 1, December 2022
- Hanane Khatouri
, Tariq Benamara, Piotr Breitkopf, Jean Demange:
Metamodeling techniques for CPU-intensive simulation-based design optimization: a survey. 1 - Mohammad Javad Kazemzadeh-Parsi, Amine Ammar, Francisco Chinesta:
Domain decomposition involving subdomain separable space representations for solving parametric problems in complex geometries. 2 - Nissrine Akkari, Fabien Casenave, David Ryckelynck, Christian Rey:
An updated Gappy-POD to capture non-parameterized geometrical variation in fluid dynamics problems. 3 - Morgane Chapelier, Robin Bouclier, Jean-Charles Passieux:
Spline-based specimen shape optimization for robust material model calibration. 4 - Fabio Giampaolo, Mariapia De Rosa, Pian Qi, Stefano Izzo, Salvatore Cuomo:
Physics-informed neural networks approach for 1D and 2D Gray-Scott systems. 5 - Jhouben Cuesta-Ramirez, Rodolphe Le Riche, Olivier Roustant, Guillaume Perrin, Cédric Durantin, Alain Glière:
A comparison of mixed-variables Bayesian optimization approaches. 6 - Harald Willmann, Wolfgang A. Wall:
Inverse analysis of material parameters in coupled multi-physics biofilm models. 7 - Jan Oldenburg, Finja Borowski, Alper Öner, Klaus-Peter Schmitz, Michael Stiehm:
Geometry aware physics informed neural network surrogate for solving Navier-Stokes equation (GAPINN). 8 - Nora Hagmeyer, Matthias Mayr, Ivo Steinbrecher, Alexander Popp:
One-way coupled fluid-beam interaction: capturing the effect of embedded slender bodies on global fluid flow and vice versa. 9 - Sebastián Cedillo, Ana-Gabriela Núñez, Esteban Sánchez-Cordero, Luis Timbe, Esteban Samaniego, Andrés Alvarado Martínez:
Physics-Informed Neural Network water surface predictability for 1D steady-state open channel cases with different flow types and complex bed profile shapes. 10 - E. Rajasekhar Nicodemus:
A methodology to assess and improve the physics consistency of an artificial neural network regression model for engineering applications. 11 - Valerie A. Martin, Reuben H. Kraft, Thomas H. Hannah, Stephen Ellis:
An energy-based study of the embedded element method for explicit dynamics. 12 - Terrin Stachiw, Alexander Crain, Joseph Ricciardi:
A physics-based neural network for flight dynamics modelling and simulation. 13 - Yinghan Wu, Kaixuan Shao, Francesco Piccialli, Gang Mei:
Numerical modeling of the propagation process of landslide surge using physics-informed deep learning. 14 - Miguel Masó, Alessandro Franci, Ignasi de-Pouplana, Alejandro Cornejo, Eugenio Oñate:
A Lagrangian-Eulerian procedure for the coupled solution of the Navier-Stokes and shallow water equations for landslide-generated waves. 15 - Veronika Singer, Klaus B. Sautter, Antonia Larese, Roland Wüchner, Kai-Uwe Bletzinger:
A partitioned material point method and discrete element method coupling scheme. 16 - Manyu Xiao, Jun Ma, Dongcheng Lu, Balaji Raghavan, Weihong Zhang:
Stress-constrained topology optimization using approximate reanalysis with on-the-fly reduced order modeling. 17 - Christian Burkhardt, Paul Steinmann, Julia Mergheim:
Thermo-mechanical simulations of powder bed fusion processes: accuracy and efficiency. 18 - Brubeck L. Freeman:
A multi-point constraint unfitted finite element method. 19 - Jonghyuk Baek, Ryan T. Schlinkman, Frank N. Beckwith, Jiun-Shyan Chen:
A deformation-dependent coupled Lagrangian/semi-Lagrangian meshfree hydromechanical formulation for landslide modeling. 20 - Francisco Chinesta, Elías Cueto:
Empowering engineering with data, machine learning and artificial intelligence: a short introductive review. 21 - Sergio R. Idelsohn, Juan M. Giménez, Norberto M. Nigro:
The Pseudo-Direct Numerical Simulation Method considered as a Reduced Order Model. 22 - Muhammad Altaf Khattak, Mian Ilyas Ahmad, Lihong Feng, Peter Benner:
Multivariate moment-matching for model order reduction of quadratic-bilinear systems using error bounds. 23 - Harald Willmann, Jonas Nitzler, Sebastian Brandstaeter, Wolfgang A. Wall:
Bayesian calibration of coupled computational mechanics models under uncertainty based on interface deformation. 24
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