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Found 48 papers, 8 papers with code
A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy
We believe this study has the potential to advance the practical application of phone-based ambient temperature measurement, facilitating energy-saving efforts in buildings.
Intelligent Chemical Purification Technique Based on Machine Learning
We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain.
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.
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.
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.
Crack-Net: Prediction of Crack Propagation in Composites
Computational solid mechanics has become an indispensable approach in engineering, and numerical investigation of fracture in composites is essential as composites are widely used in structural applications.
Physics-constrained robust learning of open-form PDEs 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.
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.
Retention Time Prediction for Chromatographic Enantioseparation by Quantile Geometry-enhanced Graph Neural Network
A new research framework is proposed to incorporate machine learning techniques into the field of experimental chemistry to facilitate chromatographic enantioseparation.
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).
Model-based Reinforcement Learning
reinforcement-learning
+1
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
The PIC is also employed to discover unrevealed macroscale governing equations from microscopic simulation data in an actual physical scene.
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.
Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model
In this work, a novel coupled theory-guided neural network (TgNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy.
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.
Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks
To realize accuracy and efficiency simultaneously in battery modeling, we propose to build a data-driven surrogate for a battery system while incorporating the underlying physics as constraints.
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net
Furthermore, based on the constructed TgU-net surrogate, a data assimilation method is employed to identify the physical process and parameters simultaneously.
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.
Deep learning based closed-loop optimization of geothermal reservoir production
In this work, we propose a closed-loop optimization framework, based on deep learning surrogates, for the well control optimization of geothermal reservoirs.
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.
Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network
The results show that the deep learning method can provide equivalent upscaling accuracy to the numerical method, and efficiency can be improved significantly compared to numerical upscaling.
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network
The surrogate models are used to conduct uncertainty quantification considering a stochastic permeability field, as well as to infer unknown permeability information based on limited well production data and observation data of formation properties.
Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network
Pressure and saturation are coupled with each other in the governing equations, and thus the two networks are also mutually conditioned in the training process by the discretized governing equations, which also increases the difficulty of model training.
An Adaptive Deep Learning Framework for Day-ahead Forecasting of Photovoltaic Power Generation
Consequently, to improve day-ahead PVPG forecasting accuracy, as well as eliminate the impacts of concept drift, this paper proposes an adaptive LSTM (AD-LSTM) model, which is a DL framework that can not only acquire general knowledge from historical data, but also dynamically learn specific knowledge from newly-arrived data.
Constructing Sub-scale Surrogate Model for Proppant Settling in Inclined Fractures from Simulation Data with Multi-fidelity Neural Network
The results demonstrate that constructing the settling surrogate with the MFNN can reduce the need for high-fidelity data and thus computational cost by 80%, while the accuracy lost is less than 5% compared to a high-fidelity surrogate.
RockGPT: Reconstructing three-dimensional digital rocks from single two-dimensional slice from the perspective of video generation
In order to obtain diverse reconstructions, the discrete latent codes are modeled using conditional GPT in an autoregressive manner, while incorporating conditional information from a given slice, rock type, and porosity.
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.
Robust discovery of partial differential equations in complex situations
In the framework, a preliminary result of potential terms provided by the deep learning-genetic algorithm is added into the loss function of the PINN as physical constraints to improve the accuracy of derivative calculation.
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.
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.
Digital rock reconstruction with user-defined properties using conditional generative adversarial networks
In fact, the proposed framework can realize the targets of MPS and TPS simultaneously by incorporating high-order information directly from rock images with the GANs scheme, while preserving low-order counterparts through conditioning.
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data
Our proposed algorithm is also able to discover PDEs with high-order derivatives or heterogeneous parameters accurately with sparse and noisy data.
Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling
In order to achieve the theory-guided training, the governing equations of the studied problems can be discretized and the finite difference scheme of the equations can be embedded into the training of CNN.
Weak Form Theory-guided Neural Network (TgNN-wf) for Deep Learning of Subsurface Single and Two-phase Flow
In the weak form, high order derivatives in the PDE can be transferred to the test functions by performing integration-by-parts, which reduces computational error.
A Lagrangian Dual-based Theory-guided Deep Neural Network
In this paper, the Lagrangian dual-based TgNN (TgNN-LD) is proposed to improve the effectiveness of TgNN.
Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example
The first category is deep-learning surrogate-based inversion methods, in which the Theory-guided Neural Network (TgNN) is constructed as a deep-learning surrogate for problems with uncertain model parameters.
Deep Learning of Dynamic Subsurface Flow via Theory-guided Generative Adversarial Network
In this study, a theory-guided generative adversarial network (TgGAN) is proposed to solve dynamic partial differential equations (PDEs).
Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data
Next, genetic algorithm is utilized to discover the form of PDEs and corresponding coefficients with an incomplete candidate library.
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.
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.
Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network
The trained neural network can predict solutions of subsurface flow problems with new stochastic parameters.
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
In the proposed framework, a deep neural network that is trained with available data of a physical problem is utilized to generate meta-data and calculate derivatives, and the genetic algorithm is then employed to discover the underlying PDE.
Deep Learning of Subsurface Flow via Theory-guided Neural Network
The TgNN can achieve higher accuracy than the ordinary Artificial Neural Network (ANN) because the former provides physically feasible predictions and can be more readily generalized beyond the regimes covered with the training data.
DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data
However, prior to achieving this goal, major challenges remain to be resolved, including learning PDE under noisy data and limited discrete data.
Identification of physical processes via combined data-driven and data-assimilation methods
Using the training data set, a data-driven method is developed to learn the governing equation of the considered physical problem by identifying the occurred (or dominated) processes and selecting the proper empirical model.
