[go: up one dir, main page]

Nagda et al., 2024 - Google Patents

Pits: Physics-informed transformers for predicting chemical phenomena

Nagda et al., 2024

View PDF
Document ID
11313996582174787022
Author
Nagda M
Ostheimer P
Specht T
Rhein F
Jirasek F
Kloft M
Fellenz S
Publication year
Publication venue
ECML/PKDD, Workshop on Machine Learning for Chemistry and Chemical Engineering

External Links

Snippet

Accurate prediction of chemical phenomena is crucial for optimizing and understanding chemical processes. Physics-Informed Neural Networks (PINNs) have emerged as a promising data-driven method for predicting chemical phenomena using deep learning …
Continue reading at ml4cce-ecml.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for programme control, e.g. control unit
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric

Similar Documents

Publication Publication Date Title
Zhao et al. Pinnsformer: A transformer-based framework for physics-informed neural networks
Ge et al. Distributed PCA model for plant-wide process monitoring
Xu et al. Data cleaning in the process industries
Miao et al. Time neighborhood preserving embedding model and its application for fault detection
Shittu et al. A systematic review of structural reliability methods for deformation and fatigue analysis of offshore jacket structures
Zhong et al. Quality-related statistical process monitoring method based on global and local partial least-squares projection
Yin et al. Mutual information–dynamic stacked sparse autoencoders for fault detection
Huang et al. Gaussian and non-Gaussian double subspace statistical process monitoring based on principal component analysis and independent component analysis
Pan et al. Robust principal component pursuit for fault detection in a blast furnace process
Zhao et al. Comprehensive subspace decomposition with analysis of between-mode relative changes for multimode process monitoring
Zhong et al. Online quality prediction of industrial terephthalic acid hydropurification process using modified regularized slow-feature analysis
Shang et al. Recursive fault detection and identification for time-varying processes
Zhou et al. Quality-relevant fault monitoring based on locally linear embedding orthogonal projection to latent structure
Chen et al. Spatiotemporal modeling for distributed parameter system under sparse sensing
Chen et al. Integrating virtual sample generation with input-training neural network for solving small sample size problems: application to purified terephthalic acid solvent system
Ma et al. Extracting dynamic features with switching models for process data analytics and application in soft sensing
Peng et al. A multilogic probabilistic signed directed graph fault diagnosis approach based on Bayesian inference
Zhan et al. Enhanced fault detection based on ensemble global–local preserving projections with quantitative global–local structure analysis
Lovelett et al. Some manifold learning considerations toward explicit model predictive control
Yan et al. Quality-driven autoencoder for nonlinear quality-related and process-related fault detection based on least-squares regularization and enhanced statistics
Zhang et al. Data-driven robust optimization based on principle component analysis and cutting plane methods
Chen et al. Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils
Zhou et al. Data-efficient constrained learning for optimal tracking of batch processes
Tian et al. Fault detection and diagnosis for non-gaussian processes with periodic disturbance based on AMRA-ICA
Lejarza et al. Discovering governing equations via moving horizon learning: The case of reacting systems