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Le CSMA soutient financièrement et scientifiquement depuis 2012, le journal OpenAccess Advanced Modeling and Simulation in Engineering Sciences. Ce dernier, proposé par l’éditeur Springer, avait pour éditeur en chef Pierre Ladevèze jusqu’en mai 2024. Francisco Chinesta assure la continuité depuis cette date.
Le processus de référencement d’un nouveau journal est particulièrement long. Le succès du journal a permis l’obtention de son référencement en 2023.
Liste HAL des articles publiés dans la revue AMSES
280 documents
- Yinghan Wu, Kaixuan Shao, Francesco Piccialli, Gang Mei. Numerical modeling of the propagation process of landslide surge using physics-informed deep learning. Advanced Modeling and Simulation in Engineering Sciences, 2022, Physics-Informed and Scientific Machine Learning, 9 (1), pp.1-15. ⟨10.1186/s40323-022-00228-6⟩. ⟨hal-04456076⟩
- Terrin Stachiw, Alexander Crain, Joseph Ricciardi. A physics-based neural network for flight dynamics modelling and simulation. Advanced Modeling and Simulation in Engineering Sciences, 2022, Physics-Informed and Scientific Machine Learning, 9 (1), pp.1-20. ⟨10.1186/s40323-022-00227-7⟩. ⟨hal-04456077⟩
- E. Rajasekhar Nicodemus. A methodology to assess and improve the physics consistency of an artificial neural network regression model for engineering applications. Advanced Modeling and Simulation in Engineering Sciences, 2022, Physics-Informed and Scientific Machine Learning, 9 (1), pp.1-29. ⟨10.1186/s40323-022-00224-w⟩. ⟨hal-04456078⟩
- Valerie A. Martin, Reuben H. Kraft, Thomas H. Hannah, Stephen Ellis. An energy-based study of the embedded element method for explicit dynamics. Advanced Modeling and Simulation in Engineering Sciences, 2022, 9 (1), pp.1-18. ⟨10.1186/s40323-022-00223-x⟩. ⟨hal-04513972⟩
- Sebastián Cedillo, Ana Gabriela Núñez, Esteban Sánchez-Cordero, Luis Timbe, Esteban Samaniego, et al.. Physics-Informed Neural Network water surface predictability for 1D steady-state open channel cases with different flow types and complex bed profile shapes. Advanced Modeling and Simulation in Engineering Sciences, 2022, Physics-Informed and Scientific Machine Learning, 9 (1), pp.1-23. ⟨10.1186/s40323-022-00226-8⟩. ⟨hal-04458286⟩
- 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. Advanced Modeling and Simulation in Engineering Sciences, 2022, 9 (1), pp.1-30. ⟨10.1186/s40323-022-00222-y⟩. ⟨hal-04513974⟩
- Jan Oldenburg, Finja Borowski, Alper Öner, Klaus Peter Schmitz, Michael Stiehm. Geometry aware physics informed neural network surrogate for solving Navier–Stokes equation (GAPINN). Advanced Modeling and Simulation in Engineering Sciences, 2022, 9 (1), pp.1-15. ⟨10.1186/s40323-022-00221-z⟩. ⟨hal-04513973⟩
- Harald Willmann, Wolfgang A. Wall. Inverse analysis of material parameters in coupled multi-physics biofilm models. Advanced Modeling and Simulation in Engineering Sciences, 2022, 9 (1), pp.1-32. ⟨10.1186/s40323-022-00220-0⟩. ⟨hal-04513976⟩
- Fabio Giampaolo, Mariapia de Rosa, Pian Qi, Stefano Izzo, Salvatore Cuomo. Physics-informed neural networks approach for 1D and 2D Gray-Scott systems. Advanced Modeling and Simulation in Engineering Sciences, 2022, Physics-Informed and Scientific Machine Learning Francesco Piccialli, 9 (1), pp.1-17. ⟨10.1186/s40323-022-00219-7⟩. ⟨hal-04456081⟩
- Hanane Khatouri, Tariq Benamara, Piotr Breitkopf, Jean Demange. Metamodeling techniques for CPU-intensive simulation-based design optimization: a survey. Advanced Modeling and Simulation in Engineering Sciences, 2022, Efficient strategies for surrogate-based optimization including multifidelity and reduced-order models, 9 (1), pp.1-31. ⟨10.1186/s40323-022-00214-y⟩. ⟨hal-04458288⟩
Documents récupérés de l'archive ouverte HAL 