# Can Transfer Neuroevolution Tractably Solve Your Differential Equations?

arXiv: https://arxiv.org/abs/2101.01998 · HTML: https://mlsanity.com/paper/2101.01998/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2021.

## Built on by (most cited first, 11 of 11)

- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
- [CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method](https://mlsanity.com/paper/2110.15832/) (2021)
- [Learning in Sinusoidal Spaces with Physics-Informed Neural Networks](https://mlsanity.com/paper/2109.09338/) (2021)
- [SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition](https://mlsanity.com/paper/2211.08760/) (2022)
- [Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities](https://mlsanity.com/paper/2501.06572/) (2025)
- [Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect](https://mlsanity.com/paper/2312.03243/) (2023)
- [NeuSpring: Neural Spring Fields for Reconstruction and Simulation of Deformable Objects from Videos](https://mlsanity.com/paper/2511.08310/) (2025)
- [Neuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results](https://mlsanity.com/paper/2212.07624/) (2022)
- [Multitask Neuroevolution for Reinforcement Learning with Long and Short Episodes](https://mlsanity.com/paper/2203.10844/) (2022)
- [Deep learning in physics: a study of dielectric quasi-cubic particles in a uniform electric field](https://mlsanity.com/paper/2105.09866/) (2021)
- [Transferable Physics-Informed Representations via Closed-Form Head Adaptation](https://mlsanity.com/paper/2604.21761/) (2026)

## Abstract (arXiv)

This paper introduces neuroevolution for solving differential equations. The solution is obtained through optimizing a deep neural network whose loss function is defined by the residual terms from the differential equations. Recent studies have focused on learning such physics-informed neural networks through stochastic gradient descent (SGD) variants, yet they face the difficulty of obtaining an accurate solution due to optimization challenges. In the context of solving differential equations, we are faced with the problem of finding globally optimum parameters of the network, instead of being concerned with out-of-sample generalization. SGD, which searches along a single gradient direction, is prone to become trapped in local optima, so it may not be the best approach here. In contrast, neuroevolution carries out a parallel exploration of diverse solutions with the goal of circumventing local optima. It could potentially find more accurate solutions with better optimized neural networks. However, neuroevolution can be slow, raising tractability issues in practice. With that in mind, a novel and computationally efficient transfer neuroevolution algorithm is proposed in this paper. Our method is capable of exploiting relevant experiential priors when solving a new problem, with adaptation to protect against the risk of negative transfer. The algorithm is applied on a variety of differential equations to empirically demonstrate that transfer neuroevolution can indeed achieve better accuracy and faster convergence than SGD. The experimental outcomes thus establish transfer neuroevolution as a noteworthy approach for solving differential equations, one that has never been studied in the past. Our work expands the resource of available algorithms for optimizing physics-informed neural networks.