An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study
arXiv:2406.14715 · doi:10.1016/j.engappai.2024.109886
Abstract
Deep Operator Networks (DeepONets) and their physics-informed variants have shown significant promise in learning mappings between function spaces of partial differential equations, enhancing the generalization of traditional neural networks. However, for highly nonlinear real-world applications like aerospace composites processing, existing models often fail to capture underlying solutions accurately and are typically limited to single input functions, constraining rapid process design development. This paper introduces an advanced physics-informed DeepONet tailored for such complex systems with multiple input functions. Equipped with architectural enhancements like nonlinear decoders and effective training strategies such as curriculum learning and domain decomposition, the proposed model handles high-dimensional design spaces with significantly improved accuracy, outperforming the vanilla physics-informed DeepONet by two orders of magnitude. Its zero-shot prediction capability across a broad design space makes it a powerful tool for accelerating composites process design and optimization, with potential applications in other engineering fields characterized by strong nonlinearity.
Accepted at the ICML 2024 Workshop on AI for Science: Scaling in AI for Scientific Discovery
References in corpus (20)
- DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
- DGM: A deep learning algorithm for solving partial differential equations
- Fourier Neural Operator for Parametric Partial Differential Equations
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domain
- Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
- A Physics Informed Neural Network for Time-Dependent Nonlinear and Higher Order Partial Differential Equations
- Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
- A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construction
- Improved generalization with deep neural operators for engineering systems: Path towards digital twin
- Wavelet neural operator: a neural operator for parametric partial differential equations
- NOMAD: Nonlinear Manifold Decoders for Operator Learning
- A Mathematical Guide to Operator Learning
- Real-Time Prediction of Gas Flow Dynamics in Diesel Engines using a Deep Neural Operator Framework
- Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions
- Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
- Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems
- HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork
- Residual fourier neural operator for thermochemical curing of composites
- FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition