Search Results for author:
Found 59 papers, 20 papers with code
Generative Discovery of Partial Differential Equations by Learning from Math Handbooks
Data driven discovery of partial differential equations (PDEs) is a promising approach for uncovering the underlying laws governing complex systems.
Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series
We propose Context-Alignment, a new paradigm that aligns TS with a linguistic component in the language environments familiar to LLMs to enable LLMs to contextualize and comprehend TS data, thereby activating their capabilities.
Revisiting PCA for time series reduction in temporal dimension
Revisiting PCA for Time Series Reduction in Temporal Dimension; Jiaxin Gao, Wenbo Hu, Yuntian Chen; Deep learning has significantly advanced time series analysis (TSA), enabling the extraction of complex patterns for tasks like classification, forecasting, and regression.
An explainable operator approximation framework under the guideline of Green's function
In this study, we introduce a novel framework, termed GreensONet, which is constructed based on the strucutre of deep operator networks (DeepONet) to learn embedded Green's functions and solve PDEs via Green's integral formulation.
Auto-Regressive Moving Diffusion Models for Time Series Forecasting
This design aligns the diffusion model's sampling procedure with the forecasting objective, resulting in an unconditional, continuous sequential diffusion TSF model.
A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems
In complex physical systems, conventional differential equations often fall short in capturing non-local and memory effects, as they are limited to local dynamics and integer-order interactions.
Teaching Video Diffusion Model with Latent Physical Phenomenon Knowledge
To address this challenge, we propose a novel method to teach video diffusion models with latent physical phenomenon knowledge, enabling the accurate generation of physically informed phenomena.
Open-Vocabulary Remote Sensing Image Semantic Segmentation
To advance OVS in earth vision and encourage reproducible research, we establish the first open-sourced OVS benchmark for remote sensing imagery, including four public remote sensing datasets.
AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model
We develop a brand new NAS search space that incorporates various data processing techniques from state-of-the-art (SOTA) TSF models and typical PVPF deep learning models.
Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning
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
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.
A Noise-robust Multi-head Attention Mechanism for Formation Resistivity Prediction: Frequency Aware LSTM
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
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.
Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression
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.
Quantifying In-Context Reasoning Effects and Memorization Effects in LLMs
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.
Promoting AI Equity in Science: Generalized Domain Prompt Learning for Accessible VLM Research
Our framework paves the way for sustainable and inclusive VLM research, transcending the barriers between academia and industry.
LLM4ED: Large Language Models for Automatic Equation Discovery
The first strategy is to take LLMs as a black-box optimizer and achieve equation self-improvement based on historical samples and their performance.
CVTN: Cross Variable and Temporal Integration for Time Series Forecasting
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).
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning
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.
A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy
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.
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution
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.
Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression
Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis.
Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data
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.
Domain Prompt Learning with Quaternion Networks
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.
Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training
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.
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting
Time series forecasting (TSF) holds significant importance in modern society, spanning numerous domains.
MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts
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.
AS-XAI: Self-supervised Automatic Semantic Interpretation for CNN
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
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
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.
Transforming organic chemistry research paradigms: moving from manual efforts to the intersection of automation and artificial intelligence
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
However, when facing the complex real-world, most of the existing methods still strongly rely on the quantity and quality of observation data.
Domain-Controlled Prompt Learning
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
Through this AI-driven knowledge discovery, we uncover previously undisclosed explicit equations that shed light on the connection between terrain features and precipitation patterns.
Physics-constrained robust learning of open-form partial differential equations from limited and noisy data
The embedding phase integrates the initially identified PDE from the discovering process as a physical constraint into the predictive model for robust training.
Worth of knowledge in deep learning
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.
Reflection Invariance Learning for Few-shot Semantic Segmentation
Few-shot semantic segmentation (FSS) aims to segment objects of unseen classes in query images with only a few annotated support images.
Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting
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.
Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation
Few-shot aerial image segmentation is a challenging task that involves precisely parsing objects in query aerial images with limited annotated support.
Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition
Classical adversarial attacks for Face Recognition (FR) models typically generate discrete examples for target identity with a single state image.
Progressively Dual Prior Guided Few-shot Semantic Segmentation
Few-shot semantic segmentation task aims at performing segmentation in query images with a few annotated support samples.
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning
Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy.
DISCOVER: Deep identification of symbolically concise open-form PDEs via enhanced reinforcement-learning
The working mechanisms of complex natural systems tend to abide by concise and profound partial differential equations (PDEs).
Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development
An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning, and is demonstrated via optimization of shale gas development.
AutoKE: An automatic knowledge embedding framework for scientific machine learning
Imposing physical constraints on neural networks as a method of knowledge embedding has achieved great progress in solving physical problems described by governing equations.
Semantic interpretation for convolutional neural networks: What makes a cat a cat?
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.
Integration of knowledge and data in machine learning
Scientific research's mandate is to comprehend and explore the world, as well as to improve it based on experience and knowledge.
Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)
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
Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed.
Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data
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
Accordingly, we can design an automatic feature crossing method to find feature interactions in DNN, and use them as cross features in LR.
Optically reconfigurable quantum spin-valley Hall effect of light in coupled nonlinear ring resonator lattice
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.
Optics
Extremize Optical Chiralities through Polarization Singularities
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.
Optics
Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
Machine learning models have been successfully used in many scientific and engineering fields.
Optical isolation induced by subwavelength spinning particle via spin-orbit interaction
Optical isolation enables nonreciprocal manipulations of light with broad applications in optical communications.
Optics
DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data
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
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
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.
Hidden-symmetry-enforced nexus points of nodal lines in layer-stacked dielectric photonic crystals
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
