Extreme precipitation nowcasting using deep generative model

Extreme precipitation can often cause serious hazards such as flooding and landslide. Both pose a threat to human lives and lead to substantial economic loss. It is crucial to develop a reliable weather forecasting system that can predict such extreme events to mitigate the effect of heavy precipitation and increase resilience to these hazards....

A goal-oriented adjoint-based optimization approach to model vortex generator flows using smooth source term distributions

Vortex generators (VGs) are typically about two orders of magnitude smaller than their host component (such as an airplane wing). For this reason, conducting a fully-resolved RANS simulation to isolate their impact on the flow field is computationally expensive. This work presents a goal-oriented adjoint-based approach to model vortex generators...

Generating multivariate load states using a conditional variational autoencoder

For planning of power systems and for the calibration of operational tools, it is essential to analyse system performance in a large range of representative scenarios. When the available historical data is limited, generative models are a promising solution, but modelling high-dimensional dependencies is challenging. In this paper, a...

Generating Contextual Load Profiles Using a Conditional Variational Autoencoder

Generating power system states that have similar distribution and dependency to the historical ones is essential for the tasks of system planning and security assessment, especially when the historical data is insufficient. In this paper, we described a generative model for load profiles of industrial and commercial customers, based on the...

Collective test: Can generative tools working autonomously create new cultures in the long run?

The term AI has entered the creative industry in the last decades. Computer based design is now omnipresent in everyday life. Will AI take over the creative industry? Can they autonomously be creative? These are some of the headlines propagated by the mainstream media. This paper will unveil the human labor behind these computer generated...

Discrete state-space active inference in nonstationary environments

Active inference is a neuroscientific theory, which states that all living systems (e.g. the human brain) minimize a quantity termed the free energy. By minimizing this free energy, living systems keep an accurate representation of the world in their internal model (learning), are provided with an optimal way of acting on the world (action...

Hierarchical Active Inference for Continuous Robot Control: Goal-Directed Hierarchical Models for Active Inference Agents

Active inference is a method for state estimation and control actions that is based on the Free Energy principle, which explains how biological agents infer the state of their environment and act upon it by maintaining a model of that environment and evaluating predictions. This method merges both action and sensory processing and is therefore a...

Discrete state-space active inference and unknown controlled state transitions

Active inference is a process theory arising from neuroscience which casts perception, action, planning and learning under one optimisation criterion: minimisation of free energy. Current literature on the implementation of discrete state-space active inference focuses on scalability, the comparison to reinforcement learning and its performance...

Predictive and generative machine learning models for photonic crystals

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods...

A Generative Neural Network Model for Speech Enhancement

Listening in noise is a challenging problem that affects the hearing capability of not only normal hearing but especially hearing impaired people. Since the last four decades, enhancing the quality and intelligibility of noise corrupted speech by reducing the effect of noise has been addressed using statistical signal processing techniques as...

A Surrogate Model for Vortex Generator Flows

A surrogate model for vortex generator flows has been created. This was done by developing a framework to generate a reduced order approximation of a discrete optimized source-term on a uniform grid. The discrete optimized source term uses the flow field of a vortex generator simulation with a body-fitted mesh (BFM) as objective. The reduced...

Several approaches to model multi-commodity dynamical flow: on a package transport network

This thesis investigates several kinds of models of describing multi-commodity dynamical flow. Three models have been developed for a spatial logistic network, on which packages are delivered by vehicles and another<br/>model was developed to describe interactions between the customers, the stores and the deliverers, which does not explicitly...

e-Discovery: Discovering fraud related e-mails using Bayesian statistical techniques

During a digital fraud investigation the search for relevant information in mailboxes of custodians is like finding a needle in a haystack. This time consuming task can, on various levels, be improved and made more efficient. Technology Assisted Review (TAR) is already one of the available machine learning algorithms that helps speeding up the...

Short-term forecasting of solar irradiance without local telemetry: A generalized model using satellite data

Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g. operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain,...

Low-velocity impact on fibre-metal laminates

Generating high-energy absorbing FML concepts is challenging because aluminium and composite failure mechanisms are acting concurrently during the impact process. Because the impact process involves energy transformation, an analytical energy balance method was used to assess the contribution of the material constituents in the low-velocity...