Search Results for author: Yuntian Chen

Found 50 papers, 13 papers with code

Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning

no code implementations7 Jun 2024 Zhongzheng Wang, Yuntian Chen, Guodong Chen, Dongxiao Zhang

This study introduces the multimodal latent dynamic (MLD) model, a deep learning framework for fast flow prediction and well control optimization in GCS.

When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain

no code implementations7 Jun 2024 Lei Xu, Yulong Chen, Yuntian Chen, Longfeng Nie, Xuetao Wei, Liang Xue, Dongxiao Zhang

Notably, as the number of data volume and the count of local epochs increases within a threshold, there is an improvement in model performance accompanied by a reduction in the variance of performance errors.

Federated Learning

A Noise-robust Multi-head Attention Mechanism for Formation Resistivity Prediction: Frequency Aware LSTM

no code implementations6 Jun 2024 Yongan Zhang, Junfeng Zhao, Jian Li, Xuanran Wang, Youzhuang Sun, Yuntian Chen, Dongxiao Zhang

The proposed FAL effectively reduces noise interference in predicting formation resistivity from cased transient electromagnetic well logging curves, better learns high-frequency features, and thereby enhances the prediction accuracy and noise resistance of the neural network model.

Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting

no code implementations6 Jun 2024 Jiaxin Gao, Qinglong Cao, Yuntian Chen, Dongxiao Zhang

PV-Client employs an ENhanced Transformer module to capture complex interactions of various features in PV systems, and utilizes a linear module to learn trend information in PV power.

energy management

Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression

no code implementations2 Jun 2024 Ding Wang, Yuntian Chen, Shiyi Chen

In this study, we introduce a genetic symbolic regression (SR) algorithm to discover an interpretable mathematical expression for the mean velocity deficit throughout the wake, a previously unavailable insight.

Symbolic Regression

Quantifying In-Context Reasoning Effects and Memorization Effects in LLMs

no code implementations20 May 2024 Siyu Lou, Yuntian Chen, Xiaodan Liang, Liang Lin, Quanshi Zhang

In this study, we propose an axiomatic system to define and quantify the precise memorization and in-context reasoning effects used by the large language model (LLM) for language generation.

Disentanglement Language Modelling +3

Promoting AI Equity in Science: Generalized Domain Prompt Learning for Accessible VLM Research

1 code implementation14 May 2024 Qinglong Cao, Yuntian Chen, Lu Lu, Hao Sun, Zhenzhong Zeng, Xiaokang Yang, Dongxiao Zhang

Our framework paves the way for sustainable and inclusive VLM research, transcending the barriers between academia and industry.

Domain Adaptation

LLM4ED: Large Language Models for Automatic Equation Discovery

no code implementations13 May 2024 Mengge Du, Yuntian Chen, Zhongzheng Wang, Longfeng Nie, Dongxiao Zhang

The first strategy is to take LLMs as a black-box optimizer and achieve equation self-improvement based on historical samples and their performance.

Equation Discovery

CVTN: Cross Variable and Temporal Integration for Time Series Forecasting

no code implementations29 Apr 2024 Han Zhou, Yuntian Chen

To tackle these challenges, this paper deconstructs time series forecasting into the learning of historical sequences and prediction sequences, introducing the Cross-Variable and Time Network (CVTN).

Multivariate Time Series Forecasting Time Series

Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning

no code implementations29 Apr 2024 Weike Peng, Jiaxin Gao, Yuntian Chen, Shengwei Wang

To ascertain the merits of the proposed FL-XGBoost method, a comparative analysis is conducted between separate and centralized models to address a classical binary classification problem in geoenergy sector.

Bayesian Optimization Binary Classification +2

A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy

no code implementations16 Apr 2024 Dayin Chen, Xiaodan Shi, Haoran Zhang, Xuan Song, Dongxiao Zhang, Yuntian Chen, Jinyue Yan

In this study, we propose a distributed phone-based ambient temperature estimation system which enables collaboration among multiple phones to accurately measure the ambient temperature in different areas of an indoor space.

Federated Learning Meta-Learning

Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution

no code implementations29 Jan 2024 Qinglong Cao, Zhengqin Xu, Chao Ma, Xiaokang Yang, Yuntian Chen

To tackle this dilemma, we comprehensively consider the flow visual properties, including the unique flow imaging principle and morphological information, and propose the first flow visual property-informed FISR algorithm.

Image Super-Resolution

Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data

no code implementations31 Dec 2023 Yuxiao Hu, Qian Li, Xiaodan Shi, Jinyue Yan, Yuntian Chen

To address these limitations, we present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales.

Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training

no code implementations CVPR 2024 Qian Li, Yuxiao Hu, Yinpeng Dong, Dongxiao Zhang, Yuntian Chen

Adversarial training is often formulated as a min-max problem, however, concentrating only on the worst adversarial examples causes alternating repetitive confusion of the model, i. e., previously defended or correctly classified samples are not defensible or accurately classifiable in subsequent adversarial training.

Domain Prompt Learning with Quaternion Networks

no code implementations CVPR 2024 Qinglong Cao, Zhengqin Xu, Yuntian Chen, Chao Ma, Xiaokang Yang

Specifically, the proposed method involves using domain-specific vision features from domain-specific foundation models to guide the transformation of generalized contextual embeddings from the language branch into a specialized space within the quaternion networks.

Contrastive Learning

MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts

no code implementations4 Dec 2023 Jiaxin Gao, Qinglong Cao, Yuntian Chen

Utilizing the MoE framework, MoE-AMC seamlessly combines the strengths of LSRM (a Transformer-based model) for handling low SNR signals and HSRM (a ResNet-based model) for high SNR signals.

Classification Time Series Analysis

AS-XAI: Self-supervised Automatic Semantic Interpretation for CNN

no code implementations2 Dec 2023 Changqi Sun, Hao Xu, Yuntian Chen, Dongxiao Zhang

Explainable artificial intelligence (XAI) aims to develop transparent explanatory approaches for "black-box" deep learning models.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data

no code implementations1 Dec 2023 Longfeng Nie, Yuntian Chen, Dongxiao Zhang, Xinyue Liu, Wentian Yuan

Specifically, the temporal and spatial characteristics of remote sensing data of the satellite Himawari-8 are extracted and fused by recurrent neural network (RNN) and convolution operation, respectively.

A knowledge-based data-driven (KBDD) framework for all-day identification of cloud types using satellite remote sensing

no code implementations1 Dec 2023 Longfeng Nie, Yuntian Chen, Mengge Du, Changqi Sun, Dongxiao Zhang

Compared with widely used semantic segmentation networks, including SegNet, PSPNet, DeepLabV3+, UNet, and ResUnet, our proposed model CldNet with an accuracy of 80. 89+-2. 18% is state-of-the-art in identifying cloud types and has increased by 32%, 46%, 22%, 2%, and 39%, respectively.

Semantic Segmentation

Transforming organic chemistry research paradigms: moving from manual efforts to the intersection of automation and artificial intelligence

no code implementations26 Nov 2023 Chengchun Liu, Yuntian Chen, Fanyang Mo

Organic chemistry is undergoing a major paradigm shift, moving from a labor-intensive approach to a new era dominated by automation and artificial intelligence (AI).

Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework

no code implementations7 Nov 2023 Dashan Zhang, Yuntian Chen, Shiyi Chen

However, when facing the complex real-world, most of the existing methods still strongly rely on the quantity and quality of observation data.

Physics-informed machine learning

Domain-Controlled Prompt Learning

1 code implementation30 Sep 2023 Qinglong Cao, Zhengqin Xu, Yuntian Chen, Chao Ma, Xiaokang Yang

Existing prompt learning methods often lack domain-awareness or domain-transfer mechanisms, leading to suboptimal performance due to the misinterpretation of specific images in natural image patterns.

Interpretable AI-Driven Discovery of Terrain-Precipitation Relationships for Enhanced Climate Insights

no code implementations27 Sep 2023 Hao Xu, Yuntian Chen, Zhenzhong Zeng, Nina Li, Jian Li, Dongxiao Zhang

Through this AI-driven knowledge discovery, we uncover previously undisclosed explicit equations that shed light on the connection between terrain features and precipitation patterns.

Precipitation Forecasting

Physics-constrained robust learning of open-form partial differential equations from limited and noisy data

1 code implementation14 Sep 2023 Mengge Du, Yuntian Chen, Longfeng Nie, Siyu Lou, Dongxiao Zhang

The embedding phase integrates the initially identified PDE from the discovering process as a physical constraint into the predictive model for robust training.

Reinforcement Learning (RL)

Worth of knowledge in deep learning

1 code implementation3 Jul 2023 Hao Xu, Yuntian Chen, Dongxiao Zhang

Our model-agnostic framework can be applied to a variety of common network architectures, providing a comprehensive understanding of the role of prior knowledge in deep learning models.

Interpretable Machine Learning

Reflection Invariance Learning for Few-shot Semantic Segmentation

no code implementations1 Jun 2023 Qinglong Cao, Yuntian Chen, Chao Ma, Xiaokang Yang

Few-shot semantic segmentation (FSS) aims to segment objects of unseen classes in query images with only a few annotated support images.

Few-Shot Semantic Segmentation Segmentation +1

Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting

1 code implementation30 May 2023 Jiaxin Gao, WenBo Hu, Yuntian Chen

Long-term time series forecasting (LTSF) is a crucial aspect of modern society, playing a pivotal role in facilitating long-term planning and developing early warning systems.

Decoder Time Series +1

Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation

1 code implementation29 May 2023 Qinglong Cao, Yuntian Chen, Chao Ma, Xiaokang Yang

Few-shot aerial image segmentation is a challenging task that involves precisely parsing objects in query aerial images with limited annotated support.

Image Segmentation Segmentation +1

Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition

1 code implementation CVPR 2023 Qian Li, Yuxiao Hu, Ye Liu, Dongxiao Zhang, Xin Jin, Yuntian Chen

Classical adversarial attacks for Face Recognition (FR) models typically generate discrete examples for target identity with a single state image.

Adversarial Attack Data Augmentation +1

Progressively Dual Prior Guided Few-shot Semantic Segmentation

no code implementations20 Nov 2022 Qinglong Cao, Yuntian Chen, Xiwen Yao, Junwei Han

Few-shot semantic segmentation task aims at performing segmentation in query images with a few annotated support samples.

Few-Shot Semantic Segmentation Segmentation +1

TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning

no code implementations5 Oct 2022 Jiaxin Gao, WenBo Hu, Dongxiao Zhang, Yuntian Chen

Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy.

Load Forecasting Scheduling +1

Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development

no code implementations20 Jun 2022 Yuntian Chen, Dongxiao Zhang, Qun Zhao, Dexun Liu

An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning, and is demonstrated via optimization of shale gas development.

BIG-bench Machine Learning Interpretable Machine Learning

AutoKE: An automatic knowledge embedding framework for scientific machine learning

1 code implementation11 May 2022 Mengge Du, Yuntian Chen, Dongxiao Zhang

Imposing physical constraints on neural networks as a method of knowledge embedding has achieved great progress in solving physical problems described by governing equations.

BIG-bench Machine Learning Neural Architecture Search +1

Semantic interpretation for convolutional neural networks: What makes a cat a cat?

no code implementations16 Apr 2022 Hao Xu, Yuntian Chen, Dongxiao Zhang

The interpretability of deep neural networks has attracted increasing attention in recent years, and several methods have been created to interpret the "black box" model.

Explainable Artificial Intelligence (XAI) Superpixels

Integration of knowledge and data in machine learning

no code implementations15 Feb 2022 Yuntian Chen, Dongxiao Zhang

Scientific research's mandate is to comprehend and explore the world, as well as to improve it based on experience and knowledge.

BIG-bench Machine Learning Common Sense Reasoning

Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

2 code implementations9 Jun 2021 Yuntian Chen, Yingtao Luo, Qiang Liu, Hao Xu, Dongxiao Zhang

Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes and predicting future responses.

Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations

no code implementations2 Jun 2021 Yingtao Luo, Qiang Liu, Yuntian Chen, WenBo Hu, Tian Tian, Jun Zhu

Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed.

Density Estimation Model Optimization

Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data

no code implementations31 May 2021 Junsheng Zeng, Hao Xu, Yuntian Chen, Dongxiao Zhang

Although deep-learning has been successfully applied in a variety of science and engineering problems owing to its strong high-dimensional nonlinear mapping capability, it is of limited use in scientific knowledge discovery.

DNN2LR: Automatic Feature Crossing for Credit Scoring

no code implementations24 Feb 2021 Qiang Liu, Zhaocheng Liu, Haoli Zhang, Yuntian Chen, Jun Zhu

Accordingly, we can design an automatic feature crossing method to find feature interactions in DNN, and use them as cross features in LR.

Feature Engineering

Optically reconfigurable quantum spin-valley Hall effect of light in coupled nonlinear ring resonator lattice

no code implementations17 Feb 2021 Haofan Yang, Jing Xu, Zhongfei Xiong, Xinda Lu, Ruo-Yang Zhang, Yuntian Chen, Shuang Zhang

In optics, various photonic topological circuits have been developed, which were based on classical emulation of either quantum spin Hall effect or quantum valley Hall effect.


Extremize Optical Chiralities through Polarization Singularities

no code implementations11 Jan 2021 Weijin Chen, Qingdong Yang, Yuntian Chen, Wei Liu

Chiral optical effects are generally quantified along some specific incident directions of exciting waves (especially for extrinsic chiralities of achiral structures) or defined as direction-independent properties by averaging the responses among all structure orientations.


Optical isolation induced by subwavelength spinning particle via spin-orbit interaction

no code implementations24 Nov 2020 Hongkang Shi, Yuqiong Cheng, Zheng Yang, Yuntian Chen, Shubo Wang

Optical isolation enables nonreciprocal manipulations of light with broad applications in optical communications.


DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data

no code implementations22 Aug 2020 Zhaocheng Liu, Qiang Liu, Haoli Zhang, Yuntian Chen

Simple classifiers, e. g., Logistic Regression (LR), are globally interpretable, but not powerful enough to model complex nonlinear interactions among features in tabular data.

Physics-constrained indirect supervised learning

no code implementations26 Apr 2020 Yuntian Chen, Dongxiao Zhang

In the training process, the model prediction is mapped to the space of value that conforms to the physical mechanism through the projection matrix, and then the model is trained based on the indirect labels.

Ensemble long short-term memory (EnLSTM) network

1 code implementation26 Apr 2020 Yuntian Chen, Dongxiao Zhang

In this study, we propose an ensemble long short-term memory (EnLSTM) network, which can be trained on a small dataset and process sequential data.

Small Data Image Classification

Hidden-symmetry-enforced nexus points of nodal lines in layer-stacked dielectric photonic crystals

no code implementations14 Mar 2020 Zhongfei Xiong, Ruo-Yang Zhang, Rui Yu, C. T. Chan, Yuntian Chen

It was recently demonstrated that the connectivities of bands emerging from zero frequency in dielectric photonic crystals are distinct from their electronic counterparts with the same space groups.

Optics Mesoscale and Nanoscale Physics

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