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ORCIDDaniel M. Faissol
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- affiliation: Lawrence Livermore National Laboratory, Computational Engineering Division, CA, USA
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2020 – today
- 2023
[c6]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:
Reinforcement Learning for Adaptive Mesh Refinement. AISTATS 2023: 5997-6014- [c5]Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden K. Petersen, Daniel M. Faissol, Robert W. Anderson:
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement. AAMAS 2023: 14-22 - 2022
- [i6]Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides
, Brendan Keith, Brenden K. Petersen, Daniel M. Faissol, Robert W. Anderson:
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement. CoRR abs/2211.00801 (2022) - 2021
- [c4]Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol:
Discovering symbolic policies with deep reinforcement learning. ICML 2021: 5979-5989 - [c3]T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen:
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. NeurIPS 2021: 24912-24923 - [i5]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:
Reinforcement Learning for Adaptive Mesh Refinement. CoRR abs/2103.01342 (2021) - [i4]Mikel Landajuela, Brenden K. Petersen, Soo K. Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol:
Improving exploration in policy gradient search: Application to symbolic optimization. CoRR abs/2107.09158 (2021) - [i3]T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen:
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding. CoRR abs/2111.00053 (2021) - 2020
- [c2]Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol:
Single Episode Policy Transfer in Reinforcement Learning. ICLR 2020
2010 – 2019
- 2019
- [j2]Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol:
Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine. J. Comput. Biol. 26(6): 597-604 (2019) - [i2]Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol:
Single Episode Policy Transfer in Reinforcement Learning. CoRR abs/1910.07719 (2019) - 2018
- [i1]Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol:
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis. CoRR abs/1802.10440 (2018) - 2016
- [c1]Richard Wheeler, Daniel M. Faissol, Claudio Santiago, Thomas Baginski, Karl Nelson:
Physics and optimal routing for urban radiation source search. MFI 2016: 379-383 - 2013
- [j1]Kevin S. Ni, Daniel M. Faissol, Thomas Edmunds, Richard Wheeler:
Exploitation of Ambiguous Cues to Infer Terrorist Activity. Decis. Anal. 10(1): 42-62 (2013)
Coauthor Index
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