Efficient Neural Network Approaches for Conditional Optimal Transport: Background and Related Work

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Efficient Neural Network Approaches for Conditional Optimal Transport: Background and Related Work
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This paper presents two neural network approaches that approximate the solutions of static and dynamic conditional optimal transport problems, respectively.

This paper is available on arxiv under CC 4.0 license. Authors: Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.edu; Ricardo Baptista, Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA and rsb@caltech.edu; Youssef Marzouk, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and ymarz@mit.

edu; Lars Ruthotto, Department of Mathematics, Emory University, Atlanta, GA and lruthotto@emory.edu; Deepanshu Verma, Department of Mathematics, Emory University, Atlanta, GA and deepanshu.verma@emory.edu. This paper is available on arxiv under CC 4.0 license. Authors: Authors: Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.

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