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Zheng Jiang

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

SSTODE: Ocean-Atmosphere Physics-Informed Neural ODEs for Sea Surface Temperature Prediction

Zheng Jiang
Wei Wang
Gaowei Zhang
Yi Wang

Sea Surface Temperature (SST) is crucial for understanding upper-ocean thermal dynamics and ocean-atmosphere interactions, which have profound economic and social impacts. While data-driven models show promise in SST prediction, their black-box nature often limits interpretability and overlooks key physical processes. Recently, physics-informed neural networks have been gaining momentum but struggle with complex ocean-atmosphere dynamics due to 1) inadequate characterization of seawater movement (e.g., coastal upwelling) and 2) insufficient integration of external SST drivers (e.g., turbulent heat fluxes). To address these challenges, we propose SSTODE, a physics-informed Neural Ordinary Differential Equations (Neural ODEs) framework for SST prediction. First, we derive ODEs from fluid transport principles, incorporating both advection and diffusion to model ocean spatiotemporal dynamics. Through variational optimization, we recover a latent velocity field that explicitly governs the temporal dynamics of SST. Building upon ODE, we introduce an Energy Exchanges Integrator (EEI)-inspired by ocean heat budget equations-to account for external forcing factors. Thus, the variations in the components of these factors provide deeper insights into SST dynamics. Extensive experiments demonstrate that SSTODE achieves state-of-the-art performances in global and regional SST forecasting benchmarks. Furthermore, SSTODE visually reveals the impact of advection dynamics, thermal diffusion patterns, and diurnal heating-cooling cycles on SST evolution. These findings demonstrate the model's interpretability and physical consistency.

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ECAI Conference 2025 Conference Paper

FenGePad-A Tangible Multi-Prompt Interactive Framework for Deep Dune Segmentation

Zheng Jiang
Haonan Kang
Zifeng Wu
Wei Wang 0353
Gaowei Zhang
Eerdun Hasi
Xiaohan Sun
Yi Wang 0013

Interactive segmentation has become critical for efficiently delineating dune boundaries from remote sensing landform images, enabling geographers to iteratively refine model predictions through minimal user guidance. However, geographers report two major challenges when working with existing tools: (1) handling segmentation around ambiguous dune boundaries forces geographers into dense, repetitive clicking, making the interaction tedious and reducing annotation efficiency; (2) conventional desktop-based annotation platforms mainly support sequential, isolated interactions, hindering the smooth, co-located collaboration necessary for dealing with difficult cases. We thus propose FenGePad, a tangible collaborative interactive segmentation framework. It supports flexible prompt types–clicks, polylines, and scribbles–designed to accommodate geographers’ diverse annotation preferences and improve annotation efficiency. To enhance model robustness and generalization, we introduce prompt generation strategies that simulate realistic annotation behaviors of geographers during training. Finally, we instantiate a tablet-based application supporting FenGePad’s tangible annotation and collaboration. Comprehensive experiments demonstrate that FenGePad achieves competitive segmentation performance while effectively improving annotation quality and collaborative efficiency. Our results demonstrate the promise of tangible interactive frameworks for applying deep learning in geographic research.

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ECAI Conference 2024 Conference Paper

FenGe-An Interactive Framework for Improving the Utility of Deep Dune Segmentation in Geographical Tasks

Zheng Jiang
Anqi Lu
Zifeng Wu
Wei Wang 0353
Gaowei Zhang
Eerdun Hasi
Yi Wang 0013

Segmenting dunes from remote sensing landforms images with deep vision models is promising by freeing geographers from manual visual interpretation tasks, making them more concentrated on the essential tasks in solving desertification challenges. However, geographers have reported that automated segmentation results may be not satisfactory though achieving high accuracy, implying there are potential gaps between pixel-level metrics and the utility in downstream geographic tasks. Therefore, pixel-wise metrics may be not proper in evaluating the deep dune segmentation in the geography domain, arising the necessity to develop domain-specific, human-centered measurements for deep dune segmentation. This paper first proposes a novel measurement based on geographers’ subjective judgments, which allows the evaluation of the alignment between deep dune segmentation models and geographical utility. We design an interactive framework integrating multiagent reinforcement learning (MARL) with geographers’ domain knowledge to improve models’ utility in the domain of geography. Our extensive experiments show that (1) our framework enables the interactive domain knowledge integration in the model-building process, and thus (2) the dune segmentation model better aligns with geographical utility, which ultimately improves the effectiveness of dune segmentation. We have deployed the framework with a number of geographers to support their various tasks including dune segmentation as a component. The results demonstrate our framework’s capabilities.

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EAAI Journal 2023 Journal Article

Soft-margin Ellipsoid generative adversarial networks

Zheng Jiang
Bin Liu
Weihua Huang

Generative adversarial networks (GANs) are of great significance for synthetizing realistic images. However, GANs are potentially unstable during the training process, posing some challenges for their development. By defining an integral probability metric (IPM) on the hypersphere, Sphere GAN enforces the discriminator to satisfy Lipschitz continuity and stabilizes the training process. Developed from Sphere GAN, a soft-margin Ellipsoid GAN is proposed for improving the quality of generated samples and the stability of training process. In the presented method, the geometric moment difference defined on the hypersphere is generalized to the hyperellipsoid. The hyperellipsoid is realized to relax the upper bound of the IPM by extending measurable functions space, thus the quality of generated samples can be improved. Furthermore, a nonlinear separating hyperellipsoid is designed to prevent the discriminator from gradient vanishing and exploding on the classification boundary. The proposed soft-margin Ellipsoid GAN is proved theoretically to have a global optimal solution, i. e. , the probability density of generated samples approaches to that of real samples infinitely when both the discriminator and the generator are optimal. The CIFAR10 and LSUN-bedrooms datasets are selected to evaluate the performance of the proposed methods. The quantitative results show that the proposed approach decreases the Fréchet inception distance (FID) on the two datasets by 8. 2% and 16. 0%, respectively. The qualitative results show that the proposed soft-margin mechanism improves the stability of the training process.

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