Cover Image

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shen, Shan, Zhang, Yibin, Rodriguez, Hector Rodriguez, Yu, Wenjian
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Systems and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Transferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits
by: Shen, Shan, et al.
Published: (2025)

Deep-Learning-Based Pre-Layout Parasitic Capacitance Prediction on SRAM Designs
by: Shen, Shan, et al.
Published: (2025)

Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning
by: Arunachalam, Ayush, et al.
Published: (2024)

Adaptive Policy Learning to Additional Tasks
by: Hao, Wenjian, et al.
Published: (2023)

Optimal Control of Nonlinear Systems with Unknown Dynamics
by: Hao, Wenjian, et al.
Published: (2023)

Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning
by: Deng, Bairong, et al.
Published: (2024)

Task-Aware Machine Unlearning and Its Application in Load Forecasting
by: Xu, Wangkun, et al.
Published: (2023)

Performance-bounded Online Ensemble Learning Method Based on Multi-armed bandits and Its Applications in Real-time Safety Assessment
by: Hu, Songqiao, et al.
Published: (2025)

Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging
by: Hirt, Sebastian, et al.
Published: (2024)

Safe and Robust Reinforcement Learning: Principles and Practice
by: Yamagata, Taku, et al.
Published: (2024)

Learning-to-solve unit commitment based on few-shot physics-guided spatial-temporal graph convolution network
by: Yang, Mei, et al.
Published: (2024)

Predictive Lagrangian Optimization for Constrained Reinforcement Learning
by: Zhang, Tianqi, et al.
Published: (2025)

LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits
by: Kochar, Dimple Vijay, et al.
Published: (2024)

CapBench: A Multi-PDK Dataset for Machine-Learning-Based Post-Layout Capacitance Extraction
by: Rodriguez, Hector R., et al.
Published: (2026)

Learning and Current Prediction of PMSM Drive via Differential Neural Networks
by: Mei, Wenjie, et al.
Published: (2024)

A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
by: Gokhale, Anand, et al.
Published: (2026)

On zero-shot learning in neural state estimation of power distribution systems
by: Berezin, Aleksandr, et al.
Published: (2024)

Symmetric Linear Dynamical Systems are Learnable from Few Observations
by: Vu, Minh, et al.
Published: (2025)

Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids
by: da Silva, Caio Fabio Oliveira, et al.
Published: (2024)

Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting
by: Garcia, Joaquim Dias, et al.
Published: (2021)

T2S-MPC: Time-Embedded Online Adaptive Model Predictive Control for Time-Varying Dynamics
by: Shen, Zeyu, et al.
Published: (2026)

A SISA-based Machine Unlearning Framework for Power Transformer Inter-Turn Short-Circuit Fault Localization
by: Liu, Nanhong, et al.
Published: (2026)

A Survey on Reinforcement Learning in Aviation Applications
by: Razzaghi, Pouria, et al.
Published: (2022)

DeepRWCap: Neural-Guided Random-Walk Capacitance Solver for IC Design
by: Rodriguez, Hector R., et al.
Published: (2025)

One-shot backpropagation for multi-step prediction in physics-based system identification -- EXTENDED VERSION
by: Donati, Cesare, et al.
Published: (2023)

A Few-Shot LLM Framework for Extreme Day Classification in Electricity Markets
by: Alghumayjan, Saud, et al.
Published: (2026)

Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning
by: Kai, Jian, et al.
Published: (2025)

Learning Genetic Circuit Modules with Neural Networks: Full Version
by: Wang, Jichi, et al.
Published: (2025)

Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains
by: Xia, Weijie, et al.
Published: (2024)

A Tutorial on Gaussian Process Learning-based Model Predictive Control
by: Wang, Jie, et al.
Published: (2024)

On the Sample Complexity of Imitation Learning for Smoothed Model Predictive Control
by: Pfrommer, Daniel, et al.
Published: (2023)

Learning Accurate Extended-Horizon Predictions of High Dimensional Trajectories
by: Gaudet, Brian, et al.
Published: (2019)

Conformal Predictive Programming for Chance Constrained Optimization
by: Zhao, Yiqi, et al.
Published: (2024)

Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications
by: Ibrahim, Sinan, et al.
Published: (2024)

Inverse Design in Distributed Circuits Using Single-Step Reinforcement Learning
by: Li, Jiayu, et al.
Published: (2025)

Quantifying and Predicting Residential Building Flexibility Using Machine Learning Methods
by: Salter, Patrick, et al.
Published: (2024)

Improved Sample Complexity of Imitation Learning for Barrier Model Predictive Control
by: Pfrommer, Daniel, et al.
Published: (2024)

Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More
by: Schaeffer, Joachim, et al.
Published: (2024)

Learning AC Power Flow Solutions using a Data-Dependent Variational Quantum Circuit
by: Le, Thinh Viet, et al.
Published: (2025)

Learning with Imperfect Models: When Multi-step Prediction Mitigates Compounding Error
by: Somalwar, Anne, et al.
Published: (2025)