Publikationen

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1. Carlos José Gonzalez Rojas; Andreas Dengel; Mateus Dias Ribeiro

   Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations

   In: Combining Artificial Intelligence and Machine Learning with Physics Sciences. AAAI Spring Symposium (AAAI SSS-2021), March 22-24, Palo Alto, California, USA, AAAI, 2021.

2. Mateus Dias Ribeiro; Abdul Rehman; Sheraz Ahmed; Andreas Dengel

   DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks

   4/2020.

3. Mateus Dias Ribeiro; Alex Mendonça Bimbato; Maurício Araújo Zanardi; José Antônio Perrella Balestieri; David P Schmidt

   Large-eddy simulation of the flow in a direct injection spark ignition engine using an open-source framework

   In: International Journal of Engine Research, Vol. 03/2020, Pages 1-22, SAGE, 2020.

4. Gavin D. Portwood; Peetak P. Mitra; Mateus Dias Ribeiro; Tan Minh Nguyen; Balasubramanya T. Nadiga; Juan A. Saenz; Michael Chertkov; Animesh Garg; Anima Anandkumar; Andreas Dengel; Richard Baraniuk; David P. Schmidt

   Turbulence forecasting via Neural ODE

   In: Second Workshop on Machine Learning and the Physical Sciences. Machine Learning and the Physical Sciences Workshop (ML4PS-2019), located at NeurIPS 2019, December 14, Vancouver, Canada, arXiv, 11/2019.

5. Peter Janas; Mateus Dias Ribeiro; Andreas Kempf; Martin Schild; Sebastian A Kaiser

   Penetration of the Flame Into the Top-Land Crevice-Large-Eddy Simulation and Experimental High-Speed Visualization

   In: JSAE/SAE 2015 International Powertrains, Fuels & Lubricants Meeting. JSAE/SAE Powertrains, Fuels & Lubricants International Meeting, September 1-4, Kyoto, Japan, Pages 1-12, SAE Technical Paper Series, SAE International, 9/2015.

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