Takip et
Mikel Landajuela
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
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
4182019
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
175*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
1342021
Nitsche-XFEM for the coupling of an incompressible fluid with immersed thin-walled structures
F Alauzet, B Fabreges, MA Fernández, M Landajuela
Computer Methods in Applied Mechanics and Engineering 301, 300-335, 2016
872016
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
842022
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
562017
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
542015
Burgers equation
M Landajuela
BCAM Internship report: Basque Center for Applied Mathematics, 2011
462011
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
342018
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
242021
SRBench++: Principled benchmarking of symbolic regression with domain-expert interpretation
FO de Franca, M Virgolin, M Kommenda, MS Majumder, M Cranmer, ...
IEEE transactions on evolutionary computation, 2024
21*2024
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
182013
Distilling Wikipedia mathematical knowledge into neural network models
JT Kim, M Landajuela, BK Petersen
arXiv preprint arXiv:2104.05930, 2021
152021
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
11*2021
Computationally restoring the potency of a clinical antibody against Omicron
TA Desautels, KT Arrildt, AT Zemla, EY Lau, F Zhu, D Ricci, S Cronin, ...
Nature 629 (8013), 878-885, 2024
102024
Splitting schemes for incompressible fluid/thin-walled structure interaction with unfitted meshes
MA Fernández, M Landajuela
Comptes Rendus. Mathématique 353 (7), 647-652, 2015
92015
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
62022
Coupling schemes and unfitted mesh methods for fluid-structure interaction
M Landajuela
Université Pierre et Marie Curie-Paris VI, 2016
62016
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
52023
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
52023
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