Fully decoupled time-marching schemes for incompressible fluid/thin-walled structure interaction. - Miguel A. Fernández; Mikel Landajuela; Marina Vidrascu (J. Comput. Phys., 2015)

Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. - Brenden K. Petersen; Mikel Landajuela; T. Nathan Mundhenk; Cláudio Prata Santiago; Sookyung Kim; Joanne Taery Kim (ICLR, 2021)

Discovering symbolic policies with deep reinforcement learning. - Mikel Landajuela; Brenden K. Petersen; Sookyung Kim; Cláudio P. Santiago; Ruben Glatt; T. Nathan Mundhenk; Jacob F. Pettit; Daniel M. Faissol (ICML, 2021)

Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. - T. Nathan Mundhenk; Mikel Landajuela; Ruben Glatt; Cláudio P. Santiago; Daniel M. Faissol; Brenden K. Petersen (NeurIPS, 2021)

Distilling Wikipedia mathematical knowledge into neural network models. - Joanne Taery Kim; Mikel Landajuela; Brenden K. Petersen (CoRR, 2021)

Improving exploration in policy gradient search: Application to symbolic optimization. - Mikel Landajuela; Brenden K. Petersen; Soo K. Kim; Cláudio P. Santiago; Ruben Glatt; T. Nathan Mundhenk; Jacob F. Pettit; Daniel M. Faissol (CoRR, 2021)

Incorporating domain knowledge into neural-guided search. - Brenden K. Petersen; Cláudio P. Santiago; Mikel Landajuela (CoRR, 2021)

Symbolic Regression via Neural-Guided Genetic Programming Population Seeding. - T. Nathan Mundhenk; Mikel Landajuela; Ruben Glatt; Cláudio P. Santiago; Daniel M. Faissol; Brenden K. Petersen (CoRR, 2021)

Intracardiac Electrical Imaging Using the 12-Lead ECG: A Machine Learning Approach Using Synthetic Data. - Mikel Landajuela; Rushil Anirudh; Joe Loscazo; Robert Blake (CinC, 2022)

A Unified Framework for Deep Symbolic Regression. - Mikel Landajuela; Chak Shing Lee; Jiachen Yang; Ruben Glatt; Cláudio P. Santiago; Ignacio Aravena; Terrell Nathan Mundhenk; Garrett Mulcahy; Brenden K. Petersen (NeurIPS, 2022)

Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. - Fabrício Olivetti de França; Marco Virgolin; Michael Kommenda; Maimuna S. Majumder; Miles D. Cranmer; Guilherme Espada; Leon Ingelse; Alcides Fonseca; Mikel Landajuela; Brenden K. Petersen; Ruben Glatt; T. Nathan Mundhenk; et al. (CoRR, 2023)

DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces. - Jacob F. Pettit; Chak Shing Lee; Jiachen Yang; Alex Ho; Daniel M. Faissol; Brenden K. Petersen; Mikel Landajuela (AAAI, 2025)

Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics. - Conor F. Hayes; Felipe Leno da Silva; Jiachen Yang; T. Nathan Mundhenk; Chak Shing Lee; Jacob F. Pettit; Claudio Santiago; Sookyung Kim; Joanne Taery Kim; Ignacio Aravena Solis; Ruben Glatt; Andre R. Goncalves; et al. (CoRR, 2025)

SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. - Fabrício Olivetti de França; Marco Virgolin; Michael Kommenda; Maimuna S. Majumder; Miles D. Cranmer; Guilherme Espada; Leon Ingelse; Alcides Fonseca; Mikel Landajuela; Brenden K. Petersen; Ruben Glatt; T. Nathan Mundhenk; et al. (IEEE Trans. Evol. Comput., 2025)