Ze Tao

Ze Tao, Jian Zhang, Haowei Li et al.

This paper proposes the Humanoid-inspired Structural Causal Model (HSCM), a novel causal framework inspired by human intelligence, designed to overcome the limitations of conventional domain generalization models. Unlike approaches that rely on statistics to capture data-label dependencies and learn distortion-invariant representations, HSCM replicates the hierarchical processing and multi-level learning of human vision systems, focusing on modeling fine-grained causal mechanisms. By disentangling and reweighting key image attributes such as color, texture, and shape, HSCM enhances generalization across diverse domains, ensuring robust performance and interpretability. Leveraging the flexibility and adaptability of human intelligence, our approach enables more effective transfer and learning in dynamic, complex environments. Through both theoretical and empirical evaluations, we demonstrate that HSCM outperforms existing domain generalization models, providing a more principled method for capturing causal relationships and improving model robustness. The code is available at https://github.com/lambett/HSCM.

Ze Tao, Darui Zhao, Fujun Liu et al.

Physics-informed learning for PDEs is surging across scientific computing and industrial simulation, yet prevailing methods face spectral bias, residual-data imbalance, and weak extrapolation. We introduce a representation-level spectral remodeling xLSTM-PINN that combines gated-memory multiscale feature extraction with adaptive residual-data weighting to curb spectral bias and strengthen extrapolation. Across four benchmarks, we integrate gated cross-scale memory, a staged frequency curriculum, and adaptive residual reweighting, and verify with analytic references and extrapolation tests, achieving markedly lower spectral error and RMSE and a broader stable learning-rate window. Frequency-domain benchmarks show raised high-frequency kernel weights and a right-shifted resolvable bandwidth, shorter high-k error decay and time-to-threshold, and narrower error bands with lower MSE, RMSE, MAE, and MaxAE. Compared with the baseline PINN, we reduce MSE, RMSE, MAE, and MaxAE across all four benchmarks and deliver cleaner boundary transitions with attenuated high-frequency ripples in both frequency and field maps. This work suppresses spectral bias, widens the resolvable band and shortens the high-k time-to-threshold under the same budget, and without altering AD or physics losses improves accuracy, reproducibility, and transferability.

Ze Tao

Deep learning has emerged as a powerful tool in scientific modeling, particularly for complex dynamical systems; however, accurately capturing age-structured population dynamics under policy-driven fertility changes remains a significant challenge due to the lack of effective integration between domain knowledge and long-term temporal dependencies. To address this issue, we propose two physics-informed deep learning frameworks--PINN and LSTM-PINN--that incorporate policy-aware fertility functions into a transport-reaction partial differential equation to simulate population evolution from 2024 to 2054. The standard PINN model enforces the governing equation and boundary conditions via collocation-based training, enabling accurate learning of underlying population dynamics and ensuring stable convergence. Building on this, the LSTM-PINN framework integrates sequential memory mechanisms to effectively capture long-range dependencies in the age-time domain, achieving robust training performance across multiple loss components. Simulation results under three distinct fertility policy scenarios-the Three-child policy, the Universal two-child policy, and the Separate two-child policy--demonstrate the models' ability to reflect policy-sensitive demographic shifts and highlight the effectiveness of integrating domain knowledge into data-driven forecasting. This study provides a novel and extensible framework for modeling age-structured population dynamics under policy interventions, offering valuable insights for data-informed demographic forecasting and long-term policy planning in the face of emerging population challenges.