Time-Invariant Neural Operators with Applications in Solving Time-Dependent PDEs

Abstract

The deep operator network (DeepONet) is one of the basic architectures for learning nonlinear operators with neural networks. However, for operators that describe the dynamic response of physical systems, DeepONet does not naturally respect fundamental time properties, including time causality and time invariance. We propose time-invariant neural operator (TINO) to overcome these limitations, by creating the connection between time-delay neural network (TDNN) and DeepONet. We then extend the proposal to solving time-dependent PDEs with initial values, which can be treated as truncated time-invariant systems, with spatial proper orthogonal decomposition (POD) implementation in the output function space. Various numerical tests comparing TINO, neural operators with time causality, and those without time properties justify the enhanced precision of the proposed neural operator frameworks.