Toward modular biological models: defining analog modules based on referent physiological mechanisms. - Brenden K. Petersen; Glen E. P. Ropella; C. Anthony Hunt (BMC Syst. Biol., 2014)

Developing a vision for executing scientifically useful virtual biomedical experiments. - Brenden K. Petersen; C. Anthony Hunt (SpringSim (ADS), 2016)

Competing Mechanistic Hypotheses of Acetaminophen-Induced Hepatotoxicity Challenged by Virtual Experiments. - Andrew K. Smith; Brenden K. Petersen; Glen E. P. Ropella; Ryan C. Kennedy; Neil Kaplowitz; Murad Ookhtens; C. Anthony Hunt (PLoS Comput. Biol., 2016)

Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis. - Brenden K. Petersen; Jiachen Yang; Will S. Grathwohl; Chase Cockrell; Claudio Santiago; Gary An; Daniel M. Faissol (CoRR, 2018)

Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models. - Michael B. Mayhew; Brenden K. Petersen; Ana Paula Sales; John D. Greene; Vincent X. Liu; Todd S. Wasson (J. Biomed. Informatics, 2018)

Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning. - Jacob F. Pettit; Ruben Glatt; Jonathan R. Donadee; Brenden K. Petersen (CoRR, 2019)

Deep symbolic regression: Recovering mathematical expressions from data via policy gradients. - Brenden K. Petersen (CoRR, 2019)

Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine. - Brenden K. Petersen; Jiachen Yang; Will S. Grathwohl; Chase Cockrell; Claudio Santiago; Gary An; Daniel M. Faissol (J. Comput. Biol., 2019)

Single Episode Policy Transfer in Reinforcement Learning. - Jiachen Yang; Brenden K. Petersen; Hongyuan Zha; Daniel M. Faissol (ICLR, 2020)

An Interactive Visualization Platform for Deep Symbolic Regression. - Joanne Taery Kim; Sookyung Kim; Brenden K. Petersen (IJCAI, 2020)

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)

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)

Reinforcement Learning for Adaptive Mesh Refinement. - Jiachen Yang; Tarik Dzanic; Brenden K. Petersen; Jun Kudo; Ketan Mittal; Vladimir Z. Tomov; Jean-Sylvain Camier; Tuo Zhao; Hongyuan Zha; Tzanio V. Kolev; Robert W. Anderson; Daniel M. Faissol (AISTATS, 2023)

Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement. - Jiachen Yang; Ketan Mittal; Tarik Dzanic; Socratis Petrides; Brendan Keith; Brenden K. Petersen; Daniel M. Faissol; Robert W. Anderson (AAMAS, 2023)

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)