Personal information

Other IDs

Scopus Author ID: 57196460702

Keywords

Building Physics, Machine learning, Neural networks

Countries

Belgium

Activities

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Employment (1)

KU Leuven: Leuven, BE

2015-10-05 to present (Department of Civil Engineering)
Employment
Source: Self-asserted source
Astrid Tijskens

Works (13)

A convolutional neural network for the hygrothermal assessment of timber frame walls

Proceedings of Building Simulation 2021
2021-09-01 | Conference paper
SOURCE-WORK-ID:

3552633
Contributors: Astrid Tijskens; staf roels; Hans Janssen
Source: check_circle
KU Leuven

Hygrothermal assessment of timber frame walls using a convolutional neural network

Building and Environment
2021-04 | Journal article
DOI: 10.1016/j.buildenv.2021.107652
Contributors: Astrid Tijskens; Staf Roels; Hans Janssen
Source: check_circle
Crossref
grade
Preferred source (of 2)‎

Neural Networks as Metamodel for Hygrothermal Simulations of Building Components – Reducing the Calculation Time of Probabilistic Assessments

2021-03-24 | Dissertation or Thesis
SOURCE-WORK-ID:

3407472
Contributors: Astrid Tijskens; staf roels
Source: check_circle
KU Leuven

The impact of a reduced training subspace on the prediction accuracy of neural networks for hygrothermal predictions

Journal of Building Performance Simulation
2021-01-02 | Journal article
DOI: 10.1080/19401493.2020.1832148
Part of ISSN: 1940-1493
Part of ISSN: 1940-1507
Source: Self-asserted source
Astrid Tijskens
grade
Preferred source (of 3)‎

Using convolutional neural networks for hygrothermal predictions to extrapolate to other external climates

E3S Web of Conferences
2020-06-30 | Conference paper
DOI: 10.1051/e3sconf/202017204001
SOURCE-WORK-ID:

3264985

Part of ISSN: 2267-1242
WOSUID:

WOS:000594033400030
Contributors: Astrid Tijskens; Hans Janssen; staf roels
Source: check_circle
KU Leuven

Neural networks for metamodelling the hygrothermal behaviour of building components

Building and Environment
2019 | Journal article
DOI: 10.1016/j.buildenv.2019.106282
EID:

2-s2.0-85069741150

Part of ISBN:

03601323
Contributors: Tijskens, A.; Roels, S.; Janssen, H.
Source: Self-asserted source
Astrid Tijskens via Scopus - Elsevier
grade
Preferred source (of 2)‎

Optimising convolutional neural networks to predict the hygrothermal performance of building components

Energies
2019 | Journal article
DOI: 10.3390/en12203966
EID:

2-s2.0-85074880746

Part of ISBN:

19961073
Contributors: Tijskens, A.; Janssen, H.; Roels, S.
Source: Self-asserted source
Astrid Tijskens via Scopus - Elsevier
grade
Preferred source (of 2)‎

Predicting the hygrothermal behaviour of building components using neural networks

MATEC Web of Conferences
2019 | Conference paper
DOI: 10.1051/matecconf/201928202036
Part of ISSN: 2261-236X
Source: Self-asserted source
Astrid Tijskens
grade
Preferred source (of 2)‎

Robust Internal Thermal Insulation of Historic Buildings - Basic probabilistic analysis of hygrothermal performance of interior insulation

2019-12-31 | Report
SOURCE-WORK-ID:

3376730
Contributors: Hans Janssen; Peggy Freudenberg; Astrid Tijskens; Tianfeng Hou
Source: check_circle
KU Leuven

Robust Internal Thermal Insulation of Historic Buildings - Metamodelling approaches for hygrothermal performance of interior insulation

2019-12-31 | Report
SOURCE-WORK-ID:

3376731
Contributors: Hans Janssen; Astrid Tijskens; Tianfeng Hou
Source: check_circle
KU Leuven

Neural networks to predict the hygrothermal response of building components in a probabilistic framework

Healthy, Intelligent and Resilient Buildings and Urban Environments
2018 | Conference paper
DOI: 10.14305/ibpc.2018.ms-6.04
SOURCE-WORK-ID:

2348708
Source: Self-asserted source
Astrid Tijskens
grade
Preferred source (of 2)‎

A simplified dynamic zone model for a probabilistic assessment of hygrothermal risks in building components

Energy Procedia
2017 | Conference paper
DOI: 10.1016/j.egypro.2017.10.012
EID:

2-s2.0-85033391962

Part of ISBN:

18766102
Contributors: Tijskens, A.; Janssen, H.; Roels, S.
Source: Self-asserted source
Astrid Tijskens via Scopus - Elsevier
grade
Preferred source (of 2)‎

Probabilistic assessment of timber frame wall compositions using a convolutional neural network

Proceedings of the 8th International Buildings Physics Conference
Conference paper
SOURCE-WORK-ID:

3553335
Contributors: Astrid Tijskens; staf roels; Hans Janssen
Source: check_circle
KU Leuven
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