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Accurate energy consumption forecasting is essential for efficient resource management and sustainability in the building sector. Deep learning models are highly successful but struggle with limited historical data and become unusable when…

The designers pre-occupation to reduce energy consumption and to achieve better thermal ambience levels, has favoured the setting up of numerous building thermal dynamic simulation programs. The progress in the modelling of phenomenas and…

Metal additive manufacturing enables unprecedented design freedom and the production of customized, complex components. However, the rapid melting and solidification dynamics inherent to metal AM processes generate heterogeneous,…

Buildings across the world contribute significantly to the overall energy consumption and are thus stakeholders in grid operations. Towards the development of a smart grid, utilities and governments across the world are encouraging smart…

Many dynamical systems of interest are nonlinear, with examples in turbulence, epidemiology, neuroscience, and finance, making them difficult to control using linear approaches. Model predictive control (MPC) is a powerful model-based…

Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations the application of…

Modeling biological soft tissue is complex in part due to material heterogeneity. Microstructural patterns, which play a major role in defining the mechanical behavior of these tissues, are both challenging to characterize, and difficult to…

Neural control is an exciting mystery which we instinctively master. Yet, researchers have a hard time explaining the motor control trajectories. Physiologically accurate biomechanical simulations can, to some extent, mimic live subjects…

FluidDyn is a project to foster open-science and open-source in the fluid dynamics community. It is thought of as a research project to channel open-source dynamics, methods and tools to do science. We propose a set of Python packages…

Building Information Modeling (BIM) describes a central data pool covering the entire life cycle of a construction project. Similarly, Building Energy Modeling (BEM) describes the process of using a 3D representation of a building as a…

Generating realistic and interactive dynamics of traffic participants according to specific instruction is critical for street scene simulation. However, there is currently a lack of a comprehensive method that generates realistic dynamics…

Buildings Automation Systems (BAS) are ubiquitous in contemporary buildings, both monitoring building conditions and managing the building system control points. At present, these controls are prescriptive and pre-determined by the design…

Buildings energy efficiency is a widely researched topic, which is rapidly gaining popularity due to rising environmental concerns and the need for energy independence. In Northern Europe heating energy alone accounts for up to 70 percent…

Human thermal comfort measurement plays a critical role in giving feedback signals for building energy efficiency. A non-invasive measuring method based on subtleness magnification and deep learning (NIDL) was designed to achieve a…

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderstorms that occur on the sub-resolution scale. Hybrid methods…

The potential of Model Predictive Control in buildings has been shown many times, being successfully used to achieve various goals, such as minimizing energy consumption or maximizing thermal comfort. However, mass deployment has thus far…

The under-representation of cloud formation is a long-standing bias associated with climate simulations. Parameterisation schemes are required to capture cloud processes within current climate models but have known biases. We overcome these…

Demand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. Current DR approaches are completely manual and rule-based or involve deriving first principles based models which are extremely…

This paper proposes a data-driven supervised machine learning (ML) for online thermal modeling of electrically excited synchronous motors (EESMs). EESMs are desired for EVs due to their high performance, efficiency, and durability at a…

Transfer Learning (TL) is currently the most effective approach for modeling building thermal dynamics when only limited data are available. TL uses a pretrained model that is fine-tuned to a specific target building. However, it remains…