mlsanityData to 14 Oct 2026

A Sequential Meta-Transfer (SMT) Learning to Combat Complexities of Physics-Informed Neural Networks: Application to Composites Autoclave Processing

arXiv 2308.06447 ↗First posted 12 Aug 2023cs.LG cs.AIcited by 3

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Abstract (arXiv)

Physics-Informed Neural Networks (PINNs) have gained popularity in solving nonlinear partial differential equations (PDEs) via integrating physical laws into the training of neural networks, making them superior in many scientific and engineering applications. However, conventional PINNs still fall short in accurately approximating the solution of complex systems with strong nonlinearity, especially in long temporal domains. Besides, since PINNs are designed to approximate a specific realization of a given PDE system, they lack the necessary generalizability to efficiently adapt to new system configurations. This entails computationally expensive re-training from scratch for any new change in the system. To address these shortfalls, in this work a novel sequential meta-transfer (SMT) learning framework is proposed, offering a unified solution for both fast training and efficient adaptation of PINNs in highly nonlinear systems with long temporal domains. Specifically, the framework decomposes PDE's time domain into smaller time segments to create "easier" PDE problems for PINNs training. Then for each time interval, a meta-learner is assigned and trained to achieve an optimal initial state for rapid adaptation to a range of related tasks. Transfer learning principles are then leveraged across time intervals to further reduce the computational cost.Through a composites autoclave processing case study, it is shown that SMT is clearly able to enhance the adaptability of PINNs while significantly reducing computational cost, by a factor of 100.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 32 of its 48 references that are in the map. First posted on arXiv 12 Aug 2023.

Builds on

32 of 48 references.

References · 32

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20 more
  1. 2018Training behavior of deep neural network in frequency domaincited by 174
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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Studycited by 6
  2. 2025GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equationscited by 2
  3. 2025Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Studycited by 0