Publications
Journal Papers
F. Leno da Silva, A. Goncalves, S. Nguyen, D. Vashchenko, R. Glatt, T. Desautels, M. Landajuela, B. Petersen, D. Faissol Language model-accelerated deep symbolic optimization. Neural Computing and Applications, 2023, pdf
F. O. de Franca, M. Virgolin, M. Kommenda, M. S. Majumder, M. Cranmer, G. Espada, L. Ingelse, A. Fonseca, M. Landajuela, B. Petersen, R. Glatt, N. Mundhenk, C. S. Lee, J. D. Hochhalter, D. L. Randall, P. Kamienny, H. Zhang, H. Zhang, A. Simon, B. Burlacu, Jaan Kasak, Meera Machado, Casper Wilstrup, W. G. La Cava, Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition . arXiv, 2023, arXiv
M. Landajuela, C. Shing Lee, J. Yang, R. Glatt, C. Santiago, T. N Mundhenk, I Aravena, G.Mulcahy, B. K. Petersen, A Unified Framework for Deep Symbolic Regression. 36th Conference on Neural Information Processing Systems, 2022, NeurIPS 2022 conference link, openreview
M. Landajuela, R. Anirudh, J. Loscazo and R. Blake Intracardiac Electrical Imaging Using the 12-Lead ECG: A Machine Learning Approach Using Synthetic Data. Computing in Cardiology (CinC) , 2022, IEEE CinC
T. N. Mundhenk, M. Landajuela, R. Glatt, C. P. Santiago, D. faissol, B. K. Petersen Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. 35th Conference on Neural Information Processing Systems, 2021, NeurIPS 2021 openreview, arxiv
M. Landajuela, B. K. Petersen, S. K. Kim, C. Santiago, R. Glatt, N. Mundhenk, J. Pettit, D.Faissol Discovering Symbolic Policies with Deep Reinforcement Learning. 38th International Conference on Machine Learning, 2021, Spotlight, ICML 2021 conference link, pdf
B. K. Petersen, M. Landajuela, T. N. Mundhenk, C. Prata Santiago, S. Kyung Kim, J. Taery Kim, Deep Symbolic Regression: Recovering Mathematical Expressions from Data via Risk-Seeking Policy Gradients. 9th International Conference on Learning Representations, 2021, ICLR 2021 conference link, arxiv talk
M. A. Fernández, M. Landajuela, Splitting schemes and unfitted-mesh methods for the coupling of an incompressible fluid with a thin-walled structure. IMA Journal of Numerical Analysis, dry098, 2019, pdf
M. Landajuela, C. Vergara, A. Gerbi, L.Dede’, L. Formaggia, A. Quarteroni, Numerical approximation of the electromechanical coupling in the left ventricle with inclusion of the Purkinje network. International Journal for Numerical Methods in Biomedical Engineering, 2018;34:e2984, pdf
M. Landajuela, M. Vidrascu, D. Chapelle, M. A. Fernández, Coupling schemes for the FSI forward prediction challenge: comparative study and validation. International Journal for Numerical Methods in Biomedical Engineering, 2040-7947, 2016, pdf
F. Alauzet, B. Fabrèges, M. A. Fernández, M. Landajuela, Nitsche-XFEM for the coupling of an incompressible fluid with immersed thin-walled structures. Computer Methods in Applied Mechanics and Engineering, 301:300 – 335, 2016, pdf
M. A. Fernández, M. Landajuela, M. Vidrascu, Fully decoupled time-marching schemes for incompressible fluid/thin-walled structure interaction. Journal of Computational Physics, 297:156-181, 2015. pdf
M. A. Fernández, M. Landajuela, Splitting schemes for incompressible fluid/thin-walled structure interaction with unfitted meshes. Comptes Rendus Mathématique, 353(7):647-652, 2015, pdf
M. A. Fernández, M. Landajuela, A fully decoupled scheme for the interaction of a thin-walled structure with an incompressible fluid. Comptes Rendus Mathématique, 351(3):161-164, 2013, pdf
Book Chapters
A. Quarteroni, C. Vergara, M. Landajuela, Mathematical and Numerical Description of the Heart Function. Imagine Math 6: Between Culture and Mathematics, 2018, book, chapter
M. A. Fernández, M. Landajuela, J. Mullaert, M. Vidrascu, Robin-Neumann schemes for incompressible fluid-structure interaction. In Domain Decomposition Methods in Science and Engineering XXII, Lecture Notes in Computer Science (LNCS), Lugano, Switzerland, 2015, book, chapter
Patents
Machine learning based reconstruction of intracardiac electrical behavior based on electrocardiograms. US 2021/0193291 A1 Jun 24, 2021. pdf
Datasets
M. Landajuela, Rushil Anirudh, Robert Blake Dataset of Simulated Intracardiac Transmembrane Voltage Recordings and ECG Signals. Lawrence Livermore National Laboratory (LLNL) Open Data Initiative. UC San Diego Library Digital Collections. 2022. link
Dissertations
M. Landajuela, Coupling schemes and unfitted mesh methods for fluid-structure interaction. PhD Thesis, Université Pierre et Marie Curie - Paris VI, 2016, pdf
Workshops
F. Leno da Silva, A. Goncalves, S. Nguyen, D. Vashchenko, R. Glatt, T. Desautels, M. Landajuela, B. Petersen, D. Faissol Leveraging Language Models to Efficiently Learn Symbolic Optimization Solutions. Proc. of the Adaptive and Learning Agents Workshop (ALA 2022), , 2022 pdf
B. K. Petersen, C. Santiago, M. Landajuela Incorporating domain knowledge into neural-guided search via in situ priors and constraints. 8th ICML Workshop on Automated Machine Learning, 2021 openreview, arxiv
M. Landajuela, B. K Petersen, S. Kim, C. Santiago, R. Glatt, N. Mundhenk, J. Pettit, D. Faissol Improving Exploration in Policy Gradient Search: Application to Symbolic Optimization. ICLR - Math-AI workshop, 2021 conference pdf, arxiv, poster
J. Taery Kim, M. Landajuela, B. K. Petersen Distilling Wikipedia mathematical knowledge into neural network models. ICLR - Math-AI workshop, 2021 conference pdf, arxiv, poster
