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Mattsson, M., Danielski, I., Olofsson, T. & Nair, G. (2025). Archetypes-based calibration for urban building energy modelling. Energy and Buildings, 343, Article ID 115843.
Open this publication in new window or tab >>Archetypes-based calibration for urban building energy modelling
2025 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 343, article id 115843Article in journal (Refereed) Published
Abstract [en]

Reducing energy use within the building sector is vital to create sustainable cities and mitigate global warming. Urban building energy modelling (UBEM) is useful to evaluate energy demand and renovation potential in districts. In this paper, an archetypes-based calibration approach for UBEM is introduced to evaluate the effect of various timescales for energy data and detail of building-related data on the energy performance of multi-residential buildings. A case district in northern Sweden was used to identify the impact of various refurbishment strategies at district-scale.

The applicability of the archetypes-based calibration approach was validated by comparing the district model performance with high-resolution energy data. Calibrating the archetypes-based model with monthly resolution data showed a similar outcome as using higher-resolution data. Further, a district model with less archetypes can reduce modelling time and complexity, while performing similarly as a model consisting of more archetypes with higher detail. The results suggest that simplifications to UBEM can be used without compromising the performance accuracy and might facilitate district modelling in situations with data limitations.

The findings in this study contribute to the knowledge on UBEM of existing districts and energy efficiency measures’ impact on district-level energy performance.

Place, publisher, year, edition, pages
Elsevier, 2025
Keywords
Building retrofitting, District energy performance, Model validation, Residential buildings, Sweden
National Category
Energy Systems
Identifiers
urn:nbn:se:umu:diva-239428 (URN)10.1016/j.enbuild.2025.115843 (DOI)2-s2.0-105005850483 (Scopus ID)
Funder
Swedish Energy Agency, 52686-1Vinnova, P2022-01000
Available from: 2025-06-02 Created: 2025-06-02 Last updated: 2025-06-02Bibliographically approved
Liu, B., Liu, P., Han, O., Lu, W. & Olofsson, T. (2025). Comparative analysis of heat transfer in polyurethane with phase change materials: advancing multi-scale modeling for energy efficiency. In: : . Paper presented at Healthy Buildings Europe 2025 Conference, Reykjavík, Iceland, June 8-11, 2025.
Open this publication in new window or tab >>Comparative analysis of heat transfer in polyurethane with phase change materials: advancing multi-scale modeling for energy efficiency
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2025 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

Polyurethane (PU) as a popular polymer is widely recognized for its exceptional thermal insulation properties, making it a critical material for applications requiring effective heat transfer management. Integrating Phase Change Materials (PCMs) into PU (PU-PCMs) has emerged as a highly effective strategy for enhancing building envelope performance, ensuring greater indoor thermal stability, and mitigating temperature fluctuations. This study introduces an Enhanced Multi-Scale Modeling approach to investigate the thermal conductivity and energy efficiency of PU-PCMs, with a particular focus on their comparative performance in subarctic and tropical climates. By combining Molecular Dynamics (MD) simulations with Finite Element Methods (FEM), the model seamlessly bridges molecular-scale interactions with macroscopic thermal behavior. Using a Representative Volume Element (RVE)-FEM framework, microscopic properties are translated into engineering-scale parameters, enabling accurate and efficient predictions of material performance across diverse climatic conditions. The findings demonstrate the capability of PU-PCMs to significantly enhance energy efficiency and indoor thermal comfort. In a case study of a single-family house, PU-PCMs achieved a 7.376% reduction in energy consumption and increased comfort hours under Stockholm's subarctic climate. Moreover, the adaptability of PU-PCMs was validated in both tropical and subarctic environments, consistently stabilizing indoor temperatures, reducing HVAC energy demands, and improving occupant comfort. These results underscore the potential of PU-PCMs as a passive thermal management solution, advancing sustainable building practices. The proposed multi-scale model offers a computationally efficient and precise tool for optimizing material design in energy-sensitive applications, reinforcing the versatility and significance of PU-PCMs across a range of climatic conditions.

Keywords
Phase change materials (PCMs), Muli-scale modelling, Building energy, Indoor thermal comfort
National Category
Energy Engineering Building materials Composite Science and Engineering Solid and Structural Mechanics
Identifiers
urn:nbn:se:umu:diva-241917 (URN)
Conference
Healthy Buildings Europe 2025 Conference, Reykjavík, Iceland, June 8-11, 2025
Funder
Swedish Energy Agency, P2021-00248The Royal Swedish Academy of Agriculture and Forestry (KSLA), GFS2023-0131, BYG2023- 0007, GFS2024-0155J. Gust. Richert stiftelse, 2023-00884Swedish Research Council Formas, 2022-01475The Kempe Foundations
Available from: 2025-07-03 Created: 2025-07-03 Last updated: 2025-07-07Bibliographically approved
Liu, B., Liu, P., Lu, W. & Olofsson, T. (2025). Explainable artificial intelligence (XAI) for material design and engineering applications: a quantitative computational framework. International Journal of Mechanical System Dynamics, 5(2), 236-265
Open this publication in new window or tab >>Explainable artificial intelligence (XAI) for material design and engineering applications: a quantitative computational framework
2025 (English)In: International Journal of Mechanical System Dynamics, ISSN 2767-1399, Vol. 5, no 2, p. 236-265Article in journal (Refereed) Published
Abstract [en]

The advancement of artificial intelligence (AI) in material design and engineering has led to significant improvements in predictive modeling of material properties. However, the lack of interpretability in machine learning (ML)-based material informatics presents a major barrier to its practical adoption. This study proposes a novel quantitative computational framework that integrates ML models with explainable artificial intelligence (XAI) techniques to enhance both predictive accuracy and interpretability in material property prediction. The framework systematically incorporates a structured pipeline, including data processing, feature selection, model training, performance evaluation, explainability analysis, and real-world deployment. It is validated through a representative case study on the prediction of high-performance concrete (HPC) compressive strength, utilizing a comparative analysis of ML models such as Random Forest, XGBoost, Support Vector Regression (SVR), and Deep Neural Networks (DNNs). The results demonstrate that XGBoost achieves the highest predictive performance ((Formula presented.)), while SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) provide detailed insights into feature importance and material interactions. Additionally, the deployment of the trained model as a cloud-based Flask-Gunicorn API enables real-time inference, ensuring its scalability and accessibility for industrial and research applications. The proposed framework addresses key limitations of existing ML approaches by integrating advanced explainability techniques, systematically handling nonlinear feature interactions, and providing a scalable deployment strategy. This study contributes to the development of interpretable and deployable AI-driven material informatics, bridging the gap between data-driven predictions and fundamental material science principles.

Place, publisher, year, edition, pages
John Wiley & Sons, 2025
Keywords
explainable artificial intelligence (XAI), high-performance concrete, material informatics, predictive modeling, science cloud deployment
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-239423 (URN)10.1002/msd2.70017 (DOI)001491092800001 ()2-s2.0-105005851142 (Scopus ID)
Funder
J. Gust. Richert stiftelse, 2023‐00884Swedish Energy Agency, P2021‐00248Swedish Research Council Formas, 2022‐01475The Royal Swedish Academy of Agriculture and Forestry (KSLA), GFS2023‐0131BYG2023‐0007GFS2024‐0155The Royal Swedish Academy of Agriculture and Forestry (KSLA), GFS2023‐0131The Royal Swedish Academy of Agriculture and Forestry (KSLA), BYG2023‐0007The Royal Swedish Academy of Agriculture and Forestry (KSLA), GFS2024‐0155
Available from: 2025-06-02 Created: 2025-06-02 Last updated: 2025-07-11Bibliographically approved
Liu, B., Liu, P., Wang, Y., Li, Z., Lv, H., Lu, W., . . . Rabczuk, T. (2025). Explainable machine learning for multiscale thermal conductivity modeling in polymer nanocomposites with uncertainty quantification. Composite structures, 370, Article ID 119292.
Open this publication in new window or tab >>Explainable machine learning for multiscale thermal conductivity modeling in polymer nanocomposites with uncertainty quantification
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2025 (English)In: Composite structures, ISSN 0263-8223, E-ISSN 1879-1085, Vol. 370, article id 119292Article in journal (Refereed) Published
Abstract [en]

Graphene-based polymer nanocomposites show great potential for thermal management, but accurately predicting their thermal conductivity remains challenging due to multiscale structural complexity and parameter uncertainty. We propose an innovative approach integrating interpretable stochastic machine learning with multiscale analysis to predict the macroscopic thermal conductivity of graphene-based polymer nanocomposites. Our bottom-up framework addresses uncertainties in meso- and macro-scale input parameters. Using Representative Volume Elements (RVEs) and Finite Element Modeling (FEM), we compute effective thermal conductivity through homogenization. Predictive modeling is powered by the XGBoost regression tree-based algorithm. To elucidate the influence of input parameters on predictions, we employ SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), providing insights into feature interactions and interpretability. Sensitivity analyses further quantify the impact of design parameters on material properties. This integrated method enhances prediction accuracy, reduces computational costs, and bridges data-driven and physical modeling, offering a scalable solution for designing advanced composite materials for thermal management applications.

Place, publisher, year, edition, pages
Elsevier, 2025
Keywords
Interpretable integrated learning, Polymeric graphene-enhanced composites (PGECs), Sensitivity analysis, Stochastic multi-scale modeling, Thermal properties
National Category
Composite Science and Engineering
Identifiers
urn:nbn:se:umu:diva-240315 (URN)10.1016/j.compstruct.2025.119292 (DOI)2-s2.0-105007553544 (Scopus ID)
Funder
J. Gust. Richert stiftelse, 2023–00884The Kempe FoundationsEU, Horizon 2020, 101016854Swedish Energy Agency, P2021-00248Swedish Research Council Formas, 2022-01475
Available from: 2025-06-24 Created: 2025-06-24 Last updated: 2025-06-24Bibliographically approved
Kurtser, P., Feng, K., Olofsson, T. & De Andres, A. (2025). One-class anomaly detection through color-to-thermal AI for building envelope inspection. Energy and Buildings, 328, Article ID 115052.
Open this publication in new window or tab >>One-class anomaly detection through color-to-thermal AI for building envelope inspection
2025 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 328, article id 115052Article in journal (Refereed) Published
Abstract [en]

Characterizing the energy performance of building components and locating anomalies is necessary for effectively refurbishing existing buildings. It is often challenging because defects in building envelopes deteriorate without being visible. Passive infrared thermography (PIRT) is a powerful tool used in building inspection. However, thermal image interpretation requires significant domain knowledge and is prone to artifacts arising from a complex interplay of factors. As a result, PIRT-based inspections require skilled professionals, and are labor-intensive and time-consuming. Artificial intelligence (AI) holds great promise to automate building inspection, but its application remains challenging because common approaches rely on extensive labeling and supervised modeling. It is recognized that there is a need for a more applicable and flexible approach to leverage AI to assist PIRT in realistic building inspections. In this study, we present a label-free method for detecting anomalies during thermographic inspection of building envelopes. It is based on the AI-driven prediction of thermal distributions from color images. Effectively the method performs as a one-class classifier of the thermal image regions with a high mismatch between the predicted and actual thermal distributions. The algorithm can learn to identify certain features as normal or anomalous by selecting the target sample used for training. The proposed method has unsupervised modeling capabilities, greater applicability and flexibility, and can be widely implemented to assist human professionals in routine building inspections or combined with mobile platforms to automate the inspection of large areas.

Place, publisher, year, edition, pages
Elsevier, 2025
Keywords
Anomaly detection, Building inspection, Color-to-thermal, GAN, Thermography
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-232595 (URN)10.1016/j.enbuild.2024.115052 (DOI)001370527900001 ()2-s2.0-85210280431 (Scopus ID)
Funder
Swedish Energy Agency, P2021-00202Swedish Energy Agency, P2022-00141Swedish Research Council Formas, 2022-01475
Available from: 2024-12-09 Created: 2024-12-09 Last updated: 2025-04-24Bibliographically approved
Liu, B., Liu, P., Lu, W. & Olofsson, T. (2025). Stochastic multiscale modeling for thermal conductivity in polymeric graphene-enhanced composites: a study in interpretable machine learning. In: Kun Zhou (Ed.), Computational and Experimental Simulations in Engineering - Proceedings of ICCES 2024 - Volume 4: . Paper presented at 30th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2024, Singapore, 3-6 August, 2024. (pp. 208-219). Springer Science+Business Media B.V.
Open this publication in new window or tab >>Stochastic multiscale modeling for thermal conductivity in polymeric graphene-enhanced composites: a study in interpretable machine learning
2025 (English)In: Computational and Experimental Simulations in Engineering - Proceedings of ICCES 2024 - Volume 4 / [ed] Kun Zhou, Springer Science+Business Media B.V., 2025, p. 208-219Conference paper, Published paper (Refereed)
Abstract [en]

We've devised an interpretable stochastic integrated machine learning method for predicting thermal conductivity in Polymeric Graphene-Enhanced Composites (PGECs) across different scales. Our approach, integrating techniques like Regression Trees methods (Gradient Boosting machine), handles uncertain parameters via a bottom-up multi-scale framework using Representative Volume Elements in Finite Element Modeling (RVE-FEM). To understand factors affecting predictions, we use the SHapley Additive exPlanations (SHAP) algorithm. This method aligns well with training data, offering promise for efficiently designing new composite materials for thermal management.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2025
Series
Mechanisms and Machine Science, ISSN 2211-0984, E-ISSN 2211-0992 ; 176
Keywords
Interpretable Integrated Learning, Polymeric graphene-enhanced composites (PGECs), Stochastic multi-scale modelling, Thermal properties
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:umu:diva-237336 (URN)10.1007/978-3-031-82907-9_17 (DOI)2-s2.0-105001287517 (Scopus ID)9783031829062 (ISBN)978-3-031-82907-9 (ISBN)
Conference
30th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2024, Singapore, 3-6 August, 2024.
Available from: 2025-04-29 Created: 2025-04-29 Last updated: 2025-04-29
Penaka, S. R., Feng, K., Olofsson, T., Rebbling, A. & Lu, W. (2024). Improved energy retrofit decision making through enhanced bottom-up building stock modelling. Energy and Buildings, 318, Article ID 114492.
Open this publication in new window or tab >>Improved energy retrofit decision making through enhanced bottom-up building stock modelling
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2024 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 318, article id 114492Article in journal (Refereed) Published
Abstract [en]

Modelling the performance of building stocks is crucial in facilitating the renovation at the building stock level. Bottom-up building stock modelling begins by detailing individual buildings and then aggregates them into stock level. Its primary advantage lies in capturing the inherent heterogeneity among distinct buildings, which enables tailored retrofitting. Naturally, this approach requires a comprehensive dataset with detailed building information such as geometry and envelope thermal properties. However, a common challenge is the incompleteness of available data in individual datasets. To address this, previous bottom-up studies have filled the missing data with representative or statistical data. Such practice could lead to homogeneous modelling of distinct buildings within the same statistical group. This limits the utilization of key ability of bottom-up building stock modelling in capturing heterogeneity, such as tailored retrofitting to explore potential retrofitting areas and strategies. To address this challenge of homogeneous modelling, we utilize data fusion framework for bottom-up building stock modelling, employing probabilistic record linkage and inverse modelling techniques to integrate multiple incomplete building performance datasets. This framework fills the missing data in one dataset with information from another, thus capturing inherent heterogeneity in the building stock. An empirical study was conducted in Umeå, Sweden, to investigate the framework's effectiveness by modelling building stock with various retrofitting strategies. This study contribution lies in enhancing bottom-up building stock modelling by capturing inherent heterogeneity, to provide tailored retrofitting solutions.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Bottom-up, Building stock modelling, Data fusion, Energy efficiency, Heterogeneity, Incomplete data, Inverse modelling, Record linkage
National Category
Other Civil Engineering
Identifiers
urn:nbn:se:umu:diva-227827 (URN)10.1016/j.enbuild.2024.114492 (DOI)001262604500001 ()2-s2.0-85197361082 (Scopus ID)
Projects
Intelligent Human-Buildings Interactions Lab: Identify, Quantify and Guide Energy-saving Behavior at University CampusRESILIENTa Energisystem Kompetenscentrum
Funder
Swedish Research Council Formas, 2022-01475Swedish Energy Agency, P2022-00141Swedish Energy Agency, 52686-1Swedish Research Council Formas, 2020-02085
Available from: 2024-07-12 Created: 2024-07-12 Last updated: 2025-04-24Bibliographically approved
Liu, B., Wang, Y., Rabczuk, T., Olofsson, T. & Lu, W. (2024). Multi-scale modeling in thermal conductivity of polyurethane incorporated with phase change materials using physics-informed neural networks. Renewable energy, 220, Article ID 119565.
Open this publication in new window or tab >>Multi-scale modeling in thermal conductivity of polyurethane incorporated with phase change materials using physics-informed neural networks
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2024 (English)In: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 220, article id 119565Article in journal (Refereed) Published
Abstract [en]

Polyurethane (PU) possesses excellent thermal properties, making it an ideal material for thermal insulation. Incorporating Phase Change Materials (PCMs) capsules into Polyurethane has proven to be an effective strategy for enhancing building envelopes. This innovative design substantially enhances indoor thermal stability and minimizes fluctuations in indoor air temperature. To investigate the thermal conductivity of the Polyurethane-Phase Change Materials foam composite, we propose a hierarchical multi-scale model utilizing Physics-Informed Neural Networks (PINNs). This model allows accurate prediction and analysis of the material’s thermal conductivity at both the meso-scale and macro-scale. By leveraging the integration of physics-based knowledge and data-driven learning offered by Physics-Informed Neural Networks, we effectively tackle inverse problems and address complex multi-scale phenomena. Furthermore, the obtained thermal conductivity data facilitates the optimization of material design. To fully consider the occupants’ thermal comfort within a building envelope, we conduct a case study evaluating the performance of this optimized material in a detached house. Simultaneously, we predict the energy consumption associated with this scenario. All outcomes demonstrate the promising nature of this design, enabling passive building energy design and significantly improving occupants’ comfort. The successful development of this Physics-Informed Neural Networks-based multi-scale model holds immense potential for advancing our understanding of Polyurethane-Phase Change Material’s thermal properties. It can contribute to the design and optimization of materials for various practical applications, including thermal energy storage systems and insulation design in advanced building envelopes.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Physics-Informed Neural Networks, Phase Change Materials, Thermal properties, Multi-scale modelling, Building energy, Indoor comfort
National Category
Computer Sciences Composite Science and Engineering Computational Mathematics Applied Mechanics Building Technologies Energy Engineering
Identifiers
urn:nbn:se:umu:diva-216853 (URN)10.1016/j.renene.2023.119565 (DOI)001122466100001 ()2-s2.0-85177878007 (Scopus ID)
Funder
EU, Horizon 2020, 101016854The Kempe Foundations, JCK-2136J. Gust. Richert stiftelse, 2023-00884Swedish Research Council, 2018-05973Swedish Research Council, 2022-06725
Available from: 2023-11-18 Created: 2023-11-18 Last updated: 2024-08-19Bibliographically approved
Li, H., Zhang, Y., Yang, C., Gao, R., Ding, F., Olofsson, T., . . . Li, A. (2024). Sleep microenvironment improvement for the acute plateau entry population through a novel nasal oxygen supply system. Building and Environment, 256, Article ID 111467.
Open this publication in new window or tab >>Sleep microenvironment improvement for the acute plateau entry population through a novel nasal oxygen supply system
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2024 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 256, article id 111467Article in journal (Refereed) Published
Abstract [en]

Most people who have moved to high-altitude areas temporarily suffer from sleep disorders. Sleep deprivation negatively affects not only people's daytime activities but also their health. However, most of the existing nonpharmaceutical intervention methods have the problems of discomfort, restricted movement, or high cost. This study involved the use of an oxygen-rich flow of air in the breathing area during sleep to fight hypoxia and aid with altitude acclimatization when people first traveled to a highland plateau. The associated nasal breathing targeted oxygen supply system (NBTOSS) was designed and optimized by numerical simulation and full-scale experiments. Blood oxygen saturation (SaO2) and pulse rate (PR) monitoring experiments were conducted on subjects exposed to hypoxia at a high altitude (Lhasa, 3646.31 m) with or without assistance from the novel oxygen system and on a lowland plain (Xi'an, 397.5 m) as a comparison. The size of the affected area, concentration target value, and oxygen consumption were used as evaluation indices. Experiments have demonstrated the feasibility of creating an oxygen-enriched microenvironment in breathing area during sleep. The results of the testing showed that the oxygen supply area was uniformly covered and that the degree of hypoxia in subjects was effectively alleviated, with average SaO2 increasing to 95% ± 1%. Maintaining oxygen levels during sleep for temporary residents of high altitudes with less oxygen consumption and minimal oxygen supply costs is discussed to provide a healthy and comfortable oxygen-enriched environment.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
High-altitude areas, Microenvironment creation, Oxygen enrichment, Personalized air distribution, Sleep environment
National Category
Physiology and Anatomy
Identifiers
urn:nbn:se:umu:diva-223231 (URN)10.1016/j.buildenv.2024.111467 (DOI)001223626100001 ()2-s2.0-85189445111 (Scopus ID)
Available from: 2024-04-19 Created: 2024-04-19 Last updated: 2025-04-24Bibliographically approved
Liu, B., Lu, W., Olofsson, T., Zhuang, X. & Rabczuk, T. (2024). Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites. Composite structures, 327, Article ID 117601.
Open this publication in new window or tab >>Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites
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2024 (English)In: Composite structures, ISSN 0263-8223, E-ISSN 1879-1085, Vol. 327, article id 117601Article in journal (Refereed) Published
Abstract [en]

We introduce an interpretable stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene-enhanced composites (PGECs). This method encompasses the propagation of uncertain input parameters from the meso to macro scale, implemented through a foundational bottom-up multi-scale framework. In this context, Representative Volume Elements in Finite Element Modeling (RVE-FEM) are employed to derive the homogenized thermal conductivity. Besides, we employ two sets of techniques: Regression-tree-based methods (Random Forest and Gradient Boosting Machine) and Neural networks-based approaches (Artificial Neural Networks and Deep Neural Networks). To ascertain the relative influence of factors on output estimations, the SHapley Additive exPlanations (SHAP) algorithm is integrated. This interpretable machine learning methodology demonstrates strong alignment with published experimental data. It holds promise as an efficient and versatile tool for designing new composite materials tailored to applications involving thermal management.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Polymeric graphene-enhanced composites (PGECs), Interpretable Integrated Learning, Stochastic multi-scale modeling, Thermal properties, Data-driven technique
National Category
Composite Science and Engineering Applied Mechanics
Identifiers
urn:nbn:se:umu:diva-215912 (URN)10.1016/j.compstruct.2023.117601 (DOI)001102527500001 ()2-s2.0-85175088621 (Scopus ID)
Funder
The Kempe Foundations, JCK-2136EU, Horizon 2020, 101016854J. Gust. Richert stiftelse, 2023-00884Swedish Research Council, 2018-05973Swedish Research Council, 2022-06725
Available from: 2023-10-29 Created: 2023-10-29 Last updated: 2024-07-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8704-8538

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