Multi-objective loss balancing for physics-informed deep learning. arXiv 2021
Strength Lab AI: A Mixture-of-Experts Deep Learning Approach for Limit State Analysis and Design of Monolithic and Laminate Structures made of Glass
The demand for transparent building envelopes, particularly glass facades, is rising in modern architecture. These facades are expected to meet multiple objectives, including aesthetic appeal, durability, quick installation, transparency, and both economic and...
Parametrische Modellierung und generatives tiefes Lernen für den Brückenentwurf
In Anbetracht der erheblichen Umweltauswirkungen des Bauwesens wird die Analyse und v. a. Optimierung der Nachhaltigkeit von Strukturen unter Beibehaltung des etablierten Zuverlässigkeitsniveaus immer wichtiger. Im Hochbausektor existieren erste Werkzeuge zur...
Heritage++, a Spatial Computing approach to Heritage Conservation
Historic structures are affected by numerous degradation processes driven by a complex system of interconnected and mutually influencing factors. Preserving these monuments is a multidisciplinary endeavour that extends beyond one-time interventions, necessitating a...
Counterfactual Image Generation for adversarially robust and interpretable Classifiers
Neural Image Classifiers are effective but inherently hard to interpret and susceptible to adversarial attacks. Solutions to both problems exist, among others, in the form of counterfactual examples generation to enhance explainability or adversarially augment...
Generative inverse design of multimodal resonant structures for locally resonant metamaterials
In the development of locally resonant metamaterials, the physical resonator design is often omitted and replaced by an idealized mass-spring system. This paper presents a novel approach for designing multimodal resonant structures, which give rise to multi-bandgap...
Predictive modelling and latent space exploration of steel profile overstrength factors using multi‐head autoencoder‐regressors
This paper investigates the suitability and interpretability of a data‐driven deep learning algorithm for multi cross sectional overstrength factor prediction. For this purpose, we first compile datasets consisting of experiments from literature on the overstrength...
Temp-AI-Estimator: Interior Temperature Prediction using Domain-Informed Deep Learning
Approximately 40% of total energy demand in the European Union is consumed by the residential buildings sector, thus also significantly contributing to carbon dioxide emissions. Circa 28% of this energy demand is attributed to space heating and cooling, primarily...
Assessment and Integration of Sustainability and Circularity Metricswithin Generative Bridge Design
Given the construction sector’s large environmental impact, analysing and optimising sustainability of a structure becomes increasingly important. The growing number of Life-Cycle Assessment (LCA) tools for buildings is however neither directly applicable nor...
Assessment and Integration of Sustainability and Circularity Metrics within Generative Bridge Design
Given the construction sector’s large environmental impact, analysing and optimising sustainability of a structure becomes increasingly important. The growing number of Life-Cycle Assessment (LCA) tools for buildings is however neither directly applicable nor...
SOUNDLAB AI Tool-Machine learning for sound insulation value predictions
Modern architecture promotes a high demand for transparent building envelopes. Typically, glass façades are designed under a variety of objectives, one of which is to meet sound insulation requirements. Reliable and fairly accurate estimation of the sound insulation...
Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges
For conceptual design, engineers rely on conventional iterative (often manual) techniques. Emerging parametric models facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive. Pure...
ntab0: Design priors for AI-augmented generative design of network tied-arch-bridges
Projects in the Architecture, Engineering and Construction (AEC) industry inherit a great complexity due to a tremendous amount of design parameters, multiple objectives, and many involved stakeholders. Especially in the conceptual design stage of bridges, an...
Mixture-of-Experts-Ensemble Meta-Learning for Physics-Informed Neural Networks
Partial Differential Equations (PDEs) arise in natural and engineering sciences to model reality and allow for numerical assessment of these phenomena. Classical numerical solutions for PDEs rely on discretisations such as eg the finite or spectral element method...
Multi-objective loss balancing for physics-informed deep learning
Physics Informed Neural Networks (PINN) are algorithms from deep learning leveraging physical laws by including partial differential equations (PDE) together with a respective set of boundary and initial conditions (BC / IC) as penalty terms into their loss function....
Artificial intelligence-finite element method-hybrids for efficient nonlinear analysis of concrete structures
Realistic structural analyses and optimisations using the non-linear finite element method are possible today yet suffer from being very time-consuming, particularly in case of reinforced concrete plates and shells. Hence such investigations are currently dismissed in...