Cover Image

Physics-informed data-driven machine health monitoring for two-photon lithography

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jia, Sixian, Dong, Zhiqiao, Shao, Chenhui
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptive few-shot learning for robust part quality classification in two-photon lithography
by: Jia, Sixian, et al.
Published: (2026)

A Unified Hierarchical Multi-Task Multi-Fidelity Framework for Data-Efficient Surrogate Modeling in Manufacturing
by: Mehta, Manan, et al.
Published: (2026)

Fast inverse lithography based on a model-driven block stacking convolutional neural network
by: Chen, Ruixiang, et al.
Published: (2024)

Multi-Modal Data Fusion for Moisture Content Prediction in Apple Drying
by: Li, Shichen, et al.
Published: (2025)

Cuffless, calibration-free hemodynamic monitoring with physics-informed machine learning models
by: Crandall, Henry, et al.
Published: (2025)

Driving behavior-guided battery health monitoring for electric vehicles using machine learning
by: Jiang, Nanhua, et al.
Published: (2023)

Hybrid Synthetic Data Generation with Domain Randomization Enables Zero-Shot Vision-Based Part Inspection Under Extreme Class Imbalance
by: Mei, Ruo-Syuan, et al.
Published: (2025)

Operational early warning of thunderstorm-driven power outages from open data: a two-stage machine learning approach
by: Stanishevska, Iryna, et al.
Published: (2025)

Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
by: Es'kin, Vasiliy A., et al.
Published: (2025)

Multi-Modal Fusion of In-Situ Video Data and Process Parameters for Online Forecasting of Cookie Drying Readiness
by: Li, Shichen, et al.
Published: (2025)

Physics-informed extreme learning machine for Terzaghi consolidation problems and interpretation of coefficient of consolidation based on CPTu data
by: Yang, He, et al.
Published: (2025)

Active transfer learning for structural health monitoring
by: Poole, J., et al.
Published: (2025)

Physics-informed sensor coverage through structure preserving machine learning
by: Shaffer, Benjamin David, et al.
Published: (2025)

PREIG: Physics-informed and Reinforcement-driven Interpretable GRU for Commodity Demand Forecasting
by: Ma, Hongwei, et al.
Published: (2025)

Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport
by: Zeng, Siqi, et al.
Published: (2024)

Equation-informed data-driven identification of flow budgets and dynamics
by: Sevryugina, Nataliya, et al.
Published: (2024)

Battery health prognosis using Physics-informed neural network with Quantum Feature mapping
by: Hossain, Muhammad Imran, et al.
Published: (2026)

Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study
by: Schmid, Lena, et al.
Published: (2023)

Adaptive debiased machine learning using data-driven model selection techniques
by: van der Laan, Lars, et al.
Published: (2023)

On the failure of ReLU activation for physics-informed machine learning
by: Rowan, Conor
Published: (2025)

An operator preconditioning perspective on training in physics-informed machine learning
by: De Ryck, Tim, et al.
Published: (2023)

Explainable machine learning for predicting shellfish toxicity in the Adriatic Sea using long-term monitoring data of HABs
by: Marzidovšek, Martin, et al.
Published: (2024)

FP64 is All You Need: Rethinking Failure Modes in Physics-Informed Neural Networks
by: Xu, Chenhui, et al.
Published: (2025)

Feature salience - not task-informativeness - drives machine learning model explanations
by: Clark, Benedict, et al.
Published: (2026)

Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction
by: Zhang, Jianyu
Published: (2025)

Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding
by: Eslaminia, Ahmadreza, et al.
Published: (2026)

Integrating mobile and fixed monitoring data for high-resolution PM2.5 mapping using machine learning
by: Xu, Rui, et al.
Published: (2025)

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
by: Feng, Jean, et al.
Published: (2023)

FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring
by: Walger, Felix, et al.
Published: (2026)

Hessian-informed machine learning interatomic potential towards bridging theory and experiments
by: Yin, Bangchen, et al.
Published: (2026)

Event-driven physics-informed operator learning for reliability analysis
by: Garg, Shailesh, et al.
Published: (2025)

Reinforcement Learning Constrained Beam Search for Parameter Optimization of Paper Drying Under Flexible Constraints
by: Chen, Siyuan, et al.
Published: (2025)

Physics-informed features in supervised machine learning
by: Lampani, Margherita, et al.
Published: (2025)

Domain-informed explainable boosting machines for trustworthy lateral spread predictions
by: Hsiao, Cheng-Hsi, et al.
Published: (2026)

Identifying topology of leaky photonic lattices with machine learning
by: Smolina, Ekaterina O., et al.
Published: (2023)

Physics-informed inference of aerial animal movements from weather radar data
by: Lippert, Fiona, et al.
Published: (2022)

Generating synthetic electronic health record data using agent-based models to evaluate machine learning robustness under mass casualty incidents
by: Reyes, Roben Delos, et al.
Published: (2026)

Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems
by: Howard, Amanda A., et al.
Published: (2024)

Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management
by: Rashid, Harun Ur, et al.
Published: (2025)

A Physics-informed machine learning model for time-dependent wave runup prediction
by: Naeini, Saeed Saviz, et al.
Published: (2024)