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Yuan Jiang

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46 papers

1 author row

EAAI Journal 2026 Journal Article

Physics-informed cross-attention operator network with hard-constrained Fourier features for heat map prediction of large-scale battery packs

Yuan Jiang
Yiyue Jiang
Zheng Liu
Pingfeng Wang

Accurate prediction of temperature distributions is essential for safe and efficient battery pack design and management. In indirect liquid cooling configurations, battery cell layouts strongly influence internal heat transfer, which complicates layout-aware heat map prediction. However, existing data-driven and physics-informed surrogate models are often constrained by scarce high-fidelity data, grid-based discretizations, and insufficient generalization across varying layouts. To address these challenges, this paper proposes a physics-informed cross-attention operator network (PI-CAON), a mesh-free neural operator for steady-state heat map prediction in large-scale battery packs. The proposed model integrates Fourier feature encoding to represent multi-frequency thermal behaviors, a hard-constrained Fourier embedding to enforce Neumann boundary conditions, and a cross-attention-based feature fusion mechanism to explicitly capture inter-cell and layout-dependent thermal interactions. By embedding the governing heat transfer physics into training loss, PI-CAON enables label-free learning whilst maintaining physical consistency. Numerical experiments on a 20-cell indirect liquid cooling battery pack demonstrate that PI-CAON achieves accurate and robust heat map predictions across diverse layout configurations, with a maximum temperature error below 0. 03 ° C. Comparative studies show that PI-CAON consistently outperforms grid-based methods, existing physics-informed neural operators, and purely data-driven baselines in both prediction accuracy and computational efficiency, highlighting its potential for battery thermal design optimization and uncertainty quantification.

Details DOI

JMLR Journal 2026 Journal Article

Reparameterized Complex-valued Neurons Can Efficiently Learn More than Real-valued Neurons via Gradient Descent

Jin-Hui Wu
Shao-Qun Zhang
Yuan Jiang
Zhi-Hua Zhou

Complex-valued neural networks potentially possess better representations and performance than real-valued counterparts when dealing with some complicated tasks such as acoustic analysis, radar image classification, etc. Despite empirical successes, it remains unknown theoretically when and to what extent complex-valued neural networks outperform real-valued ones. We take one step in this direction by comparing the learnability of real-valued neurons and complex-valued neurons via gradient descent. We theoretically show that a complex-valued neuron can learn functions expressed by any one real-valued neuron and any one complex-valued neuron with convergence rates $O(t^{-3})$ and $O(t^{-1})$ where $t$ is the iteration index of gradient descent, respectively, whereas a two-layer real-valued neural network with finite width cannot learn a single non-degenerate complex-valued neuron. We prove that a complex-valued neuron learns a real-valued neuron with rate $\Omega (t^{-3})$, exponentially slower than the linear convergence rate of learning one real-valued neuron using a real-valued neuron. We then reparameterize the phase parameter of the complex-valued neuron and prove that a reparameterized complex-valued neuron can efficiently learn a real-valued neuron with a linear convergence rate. We further verify and extend these results via simulation experiments in more general settings. [abs] [ pdf ][ bib ] &copy JMLR 2026. ( edit, beta )