Deployment of ARX Models for Thermal Forecasting in Power Electronics Boards Using WBG Semiconductors

• URL: http://arxiv.org/abs/2411.17748v1
• Date: Mon, 25 Nov 2024 14:01:17 GMT
• Title: Deployment of ARX Models for Thermal Forecasting in Power Electronics Boards Using WBG Semiconductors
• Authors: Mohammed Riadh Berramdane, Alexandre Battiston, Michele Bardi, Nicolas Blet, Benjamin Rémy, Matthieu Urbain,
• Abstract summary: ARX parametric models provide accurate temperature predictions without detailed understanding of component thickness disparities or material physical properties.<n>These models emerge as a reliable alternative to FEM simulations and conventional thermal models.
• Score: 37.69303106863453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facing the thermal management challenges of Wide Bandgap (WBG) semiconductors, this study highlights the use of ARX parametric models, which provide accurate temperature predictions without requiring detailed understanding of component thickness disparities or material physical properties, relying solely on experimental measurements. These parametric models emerge as a reliable alternative to FEM simulations and conventional thermal models, significantly simplifying system identification while ensuring high result accuracy.

Related papers

• Hybrid AI-Physical Modeling for Penetration Bias Correction in X-band InSAR DEMs: A Greenland Case Study [1.4711955189581167]
  We propose an integrated correction framework that combines parametric physical modeling with machine learning. Experiments on Greenland's ice sheet using TanDEM-X data show that the proposed hybrid model corrections significantly reduce the mean and standard deviation of DEM errors.
  arXiv  Detail & Related papers  (2025-04-11T18:24:22Z)
• Bayesian LSTM for indoor temperature modeling [0.0]
  We propose a Bayesian Long Short-Term Memory architecture for indoor temperature modeling. Our experiments across 100 real-world buildings demonstrate that the Bayesian LSTM outperforms an industrial physics-based model in predictive accuracy. This work advances data-driven heating control by balancing predictive performance with the transparency and reliability required for real-world heating MPC applications.
  arXiv  Detail & Related papers  (2025-04-04T11:07:23Z)
• Deep generative modelling of canonical ensemble with differentiable thermal properties [0.9421843976231371]
  We propose a variational modelling method with differentiable temperature for canonical ensembles. Using a deep generative model, the free energy is estimated and minimized simultaneously in a continuous temperature range. The training process requires no dataset, and works with arbitrary explicit density generative models.
  arXiv  Detail & Related papers  (2024-04-29T03:41:49Z)
• Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation [50.920911532133154]
  The intrinsic ill-posedness and ordinal-sensitive nature of monocular depth estimation (MDE) models pose major challenges to the estimation of uncertainty degree. We propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions. By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability.
  arXiv  Detail & Related papers  (2023-07-19T12:11:15Z)
• Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators [0.0]
  This paper presents a data-driven model that captures the local temperature evolution during the additive manufacturing process. It is tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition process. The results demonstrate that the model achieves high fidelity as measured by $R2$ and maintains generalizability to geometries that were not included in the training process.
  arXiv  Detail & Related papers  (2023-07-04T16:17:59Z)
• Long Horizon Temperature Scaling [90.03310732189543]
  Long Horizon Temperature Scaling (LHTS) is a novel approach for sampling from temperature-scaled joint distributions. We derive a temperature-dependent LHTS objective, and show that finetuning a model on a range of temperatures produces a single model capable of generation with a controllable long horizon temperature parameter.
  arXiv  Detail & Related papers  (2023-02-07T18:59:32Z)
• Evaluating generative models in high energy physics [7.545095780512178]
  We present the first systematic review and investigation into evaluation metrics and their sensitivity to failure modes of generative models. We propose two new metrics, the Fr'echet and kernel physics distances (FPD and KPD, respectively), and perform a variety of experiments measuring their performance. We demonstrate the efficacy of these proposed metrics in evaluating and comparing a novel attention-based generative adversarial particle transformer to the state-of-the-art message-passing generative adversarial network jet simulation model.
  arXiv  Detail & Related papers  (2022-11-18T15:36:28Z)
• Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One [83.5162421521224]
  We propose a unique method termed E-ARM for training autoregressive generative models. E-ARM takes advantage of a well-designed energy-based learning objective. We show that E-ARM can be trained efficiently and is capable of alleviating the exposure bias problem.
  arXiv  Detail & Related papers  (2022-06-26T10:58:41Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning [0.0]
  Thermal models for electric power systems are required to be both, real-time capable and of high estimation accuracy. In this work, the thermal neural network (TNN) is introduced, which unifies both, consolidated knowledge in the form of heat-transfer-based lumped- parameter models. A TNN has physically interpretable states through its state-space representation, is end-to-end trainable, and requires no material, geometry, nor expert knowledge for its design.
  arXiv  Detail & Related papers  (2021-03-30T13:15:48Z)
• Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration [57.568461777747515]
  We introduce a novel calibration method, Parametrized Temperature Scaling (PTS) We demonstrate that the performance of accuracy-preserving state-of-the-art post-hoc calibrators is limited by their intrinsic expressive power. We show with extensive experiments that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of model architectures, datasets and metrics.
  arXiv  Detail & Related papers  (2021-02-24T10:18:30Z)
• Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning [0.0]
  Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task. Overheating results in severe motor deterioration and is thus of high concern for the machine's control strategy and its design. Several machine learning (ML) models are empirically evaluated on their estimation accuracy for the task of predicting latent high-dynamic magnet temperature profiles.
  arXiv  Detail & Related papers  (2020-01-17T11:41:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.