Cover Image

Partially stochastic deep learning with uncertainty quantification for model predictive heating control

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hannula, Emma, Häkkinen, Arttu, Solonen, Antti, Uribe, Felipe, de Wiljes, Jana, Roininen, Lassi
Format:	Preprint
Published:	2025
Subjects:	Applications Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Identifiability and amortized inference limitations in Kuramoto models
by: Hannula, Emma, et al.
Published: (2026)

Evaluating consumption effects of intelligent control algorithms for district heated buildings
by: Solonen, Antti, et al.
Published: (2026)

A Log-Gaussian Cox Process with Sequential Monte Carlo for Line Narrowing in Spectroscopy
by: Härkönen, Teemu, et al.
Published: (2022)

Probabilistic multivariate statistical process control via kernel parameter uncertainty propagation
by: Duma, Zina-Sabrina, et al.
Published: (2026)

Log-Gaussian Cox Processes for Spatiotemporal Traffic Fatality Estimation in Addis Ababa
by: Abebe, Yassin Tesfaw, et al.
Published: (2024)

Surrogate modeling for uncertainty quantification in nonlinear dynamics
by: Marelli, S., et al.
Published: (2025)

A Statistical Framework and Analysis for Perfect Radar Pulse Compression
by: Chada, Neil K., et al.
Published: (2023)

Spatiotemporal Dynamics of Conflict Occurrence and Fatalities in Ethiopia: A Bayesian Model and Predictive Insights Using Event-level Data (1997--2024)
by: Abebe, Yassin Tesfaw, et al.
Published: (2025)

Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification
by: Kim, Jungho, et al.
Published: (2024)

Uncertainty quantification in automated valuation models with spatially weighted conformal prediction
by: Hjort, Anders, et al.
Published: (2023)

Rashomon perspective for measuring uncertainty in the survival predictive maintenance models
by: Yardimci, Yigitcan, et al.
Published: (2025)

Isotonic Quantile Regression Averaging for uncertainty quantification of electricity price forecasts
by: Lipiecki, Arkadiusz, et al.
Published: (2025)

Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification
by: Hart, J., et al.
Published: (2024)

Hierarchical Bayesian Modeling of Total Column Ozone: Unraveling Equatorial Variability over Ethiopia Using Satellite Data and Multisource Covariates
by: Abebe, Yassin Tesfaw, et al.
Published: (2025)

Log-Gaussian Gamma Processes for Training Bayesian Neural Networks in Raman and CARS Spectroscopies
by: Härkönen, Teemu, et al.
Published: (2023)

Optimising Kernel-based Multivariate Statistical Process Control
by: Duma, Zina-Sabrina, et al.
Published: (2025)

Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework
by: Zhang, Dashan, et al.
Published: (2023)

GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction
by: Lou, Xiayin, et al.
Published: (2024)

Density correction for multivariate spatial fields of global climate model output using deep learning
by: Majumder, Reetam, et al.
Published: (2024)

Variable importance measure for spatial machine learning models with application to air pollution exposure prediction
by: Cheng, Si, et al.
Published: (2024)

Error-controlled non-additive interaction discovery in machine learning models
by: Chen, Winston, et al.
Published: (2024)

Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments
by: Chandra, Rohitash, et al.
Published: (2024)

Causal machine learning for predicting treatment outcomes
by: Feuerriegel, Stefan, et al.
Published: (2024)

Stability of clinical prediction models developed using statistical or machine learning methods
by: Riley, Richard D, et al.
Published: (2022)

Explaining deep learning for ECG using time-localized clusters
by: Boubekki, Ahcène, et al.
Published: (2025)

A comparison between geostatistical and machine learning models for spatio-temporal prediction of PM2.5 data
by: Mohamed, Zeinab, et al.
Published: (2025)

Bayesian neural networks for predicting uncertainty in full-field material response
by: Pasparakis, George D., et al.
Published: (2024)

Uncertainty quantification for data-driven weather models
by: Bülte, Christopher, et al.
Published: (2024)

Applications of machine learning to predict seasonal precipitation for East Africa
by: Scheuerer, Michael, et al.
Published: (2024)

Bayesian inversion with Student's t priors based on Gaussian scale mixtures
by: Senchukova, Angelina, et al.
Published: (2024)

Bayesian calibration of stochastic agent based model via random forest
by: Robertson, Connor, et al.
Published: (2024)

Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning
by: Wang, Zhongzheng, et al.
Published: (2024)

The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information
by: Abler, D., et al.
Published: (2025)

Score-based diffusion models for diffuse optical tomography with uncertainty quantification
by: Schneider, Fabian, et al.
Published: (2026)

Diagnosis-based mortality prediction for intensive care unit patients via transfer learning
by: Xu, Mengqi, et al.
Published: (2025)

At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition
by: Dahal, Ashok, et al.
Published: (2024)

Quantifying uncertainty in climate projections with conformal ensembles
by: Harris, Trevor, et al.
Published: (2024)

A practical guide to estimation and uncertainty quantification of aerodynamic flows
by: Eldredge, Jeff D., et al.
Published: (2025)

Personalised dynamic super learning: an application in predicting hemodiafiltration convection volumes
by: Chatton, Arthur, et al.
Published: (2023)

Meta-models for transfer learning in source localisation
by: Bull, Lawrence A., et al.
Published: (2023)