| Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients BK Petersen, M Landajuela, TN Mundhenk, CP Santiago, SK Kim, JT Kim arXiv preprint arXiv:1912.04871, 2019 | 338 | 2019 |
| Symbolic regression via neural-guided genetic programming population seeding TN Mundhenk, M Landajuela, R Glatt, CP Santiago, DM Faissol, ... arXiv preprint arXiv:2111.00053, 2021 | 133* | 2021 |
| Discovering symbolic policies with deep reinforcement learning M Landajuela, BK Petersen, S Kim, CP Santiago, R Glatt, N Mundhenk, ... International Conference on Machine Learning, 5979-5989, 2021 | 109 | 2021 |
| Nitsche-XFEM for the coupling of an incompressible fluid with immersed thin-walled structures F Alauzet, B Fabrèges, MA Fernández, M Landajuela Computer Methods in Applied Mechanics and Engineering 301, 300-335, 2016 | 87 | 2016 |
| A unified framework for deep symbolic regression M Landajuela, CS Lee, J Yang, R Glatt, CP Santiago, I Aravena, ... Advances in Neural Information Processing Systems 35, 33985-33998, 2022 | 57 | 2022 |
| Fully decoupled time-marching schemes for incompressible fluid/thin-walled structure interaction MA Fernández, M Landajuela, M Vidrascu Journal of Computational Physics 297, 156-181, 2015 | 53 | 2015 |
| Coupling schemes for the FSI forward prediction challenge: comparative study and validation M Landajuela, M Vidrascu, D Chapelle, MA Fernández International journal for numerical methods in biomedical engineering 33 (4 …, 2017 | 52 | 2017 |
| Burgers equation M Landajuela BCAM Internship report: Basque Center for Applied Mathematics, 2011 | 42 | 2011 |
| Numerical approximation of the electromechanical coupling in the left ventricle with inclusion of the Purkinje network M Landajuela, C Vergara, A Gerbi, L Dedè, L Formaggia, A Quarteroni International journal for numerical methods in biomedical engineering 34 (7 …, 2018 | 33 | 2018 |
| Improving exploration in policy gradient search: Application to symbolic optimization M Landajuela, BK Petersen, SK Kim, CP Santiago, R Glatt, TN Mundhenk, ... arXiv preprint arXiv:2107.09158, 2021 | 22 | 2021 |
| A fully decoupled scheme for the interaction of a thin-walled structure with an incompressible fluid MA Fernández, M Landajuela Comptes Rendus. Mathématique 351 (3-4), 161-164, 2013 | 16 | 2013 |
| Distilling Wikipedia mathematical knowledge into neural network models JT Kim, M Landajuela, BK Petersen arXiv preprint arXiv:2104.05930, 2021 | 14 | 2021 |
| Interpretable symbolic regression for data science: analysis of the 2022 competition FO de França, M Virgolin, M Kommenda, MS Majumder, M Cranmer, ... arXiv preprint arXiv:2304.01117, 2023 | 12 | 2023 |
| Splitting schemes for incompressible fluid/thin-walled structure interaction with unfitted meshes MA Fernández, M Landajuela Comptes Rendus. Mathématique 353 (7), 647-652, 2015 | 9 | 2015 |
| Incorporating domain knowledge into neural-guided search via in situ priors and constraints BK Petersen, CP Santiago, M Landajuela Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2021 | 7* | 2021 |
| Coupling schemes and unfitted mesh methods for fluid-structure interaction M Landajuela Université Pierre et Marie Curie-Paris VI, 2016 | 6 | 2016 |
| Language model-accelerated deep symbolic optimization FL da Silva, A Goncalves, S Nguyen, D Vashchenko, R Glatt, T Desautels, ... Neural Computing and Applications, 1-17, 2023 | 5 | 2023 |
| Toward multi-fidelity reinforcement learning for symbolic optimization FL Silva, J Yang, M Landajuela, A Goncalves, A Ladd, D Faissol, ... Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2023 | 4 | 2023 |
| Leveraging language models to efficiently learn symbolic optimization solutions FL da Silva, A Goncalves, S Nguyen, D Vashchenko, R Glatt, T Desautels, ... Adaptive and Learning Agents (ALA) Workshop at AAMAS, 2022 | 4 | 2022 |
| Robin-Neumann schemes for incompressible fluid-structure interaction MA Fernández, M Landajuela, J Mullaert, M Vidrascu Domain decomposition methods in science and engineering XXII, 65-76, 2016 | 3 | 2016 |
Brenden K. PetersenLawrence Livermore National LaboratoryVerified email at llnl.gov
