Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning

• URL: http://arxiv.org/abs/2305.19913v2
• Date: Thu, 2 Nov 2023 14:32:26 GMT
• Title: Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning
• Authors: Francesca Bartolucci and Emmanuel de B\'ezenac and Bogdan Raoni\'c and Roberto Molinaro and Siddhartha Mishra and Rima Alaifari
• Abstract summary: This research offers a fresh take on neural operators with a framework Representation equivalent Neural Operators (ReNO) At its core is the concept of operator aliasing, which measures inconsistency between neural operators and their discrete representations. Our findings detail how aliasing introduces errors when handling different discretizations and grids and loss of crucial continuous structures.
• Score: 11.11883703395469
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, operator learning, or learning mappings between infinite-dimensional function spaces, has garnered significant attention, notably in relation to learning partial differential equations from data. Conceptually clear when outlined on paper, neural operators necessitate discretization in the transition to computer implementations. This step can compromise their integrity, often causing them to deviate from the underlying operators. This research offers a fresh take on neural operators with a framework Representation equivalent Neural Operators (ReNO) designed to address these issues. At its core is the concept of operator aliasing, which measures inconsistency between neural operators and their discrete representations. We explore this for widely-used operator learning techniques. Our findings detail how aliasing introduces errors when handling different discretizations and grids and loss of crucial continuous structures. More generally, this framework not only sheds light on existing challenges but, given its constructive and broad nature, also potentially offers tools for developing new neural operators.

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