Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 7 of 7 for “"AI for Science"”.
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Towards Efficient AI for Science in Scalable and High Performance Distributed System
Artificial intelligence (AI) has seen rapid development over the last few decades, significantly impacting various domains such as computer vision and natural language processing. In recent years, machine learning methods have been increasingly applied to the scientific discovery process, …
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Learning from Structured Data with Weak Supervision
In all science, inquiry proceeds based on observation and experimentation, exercising informed judgement and developing hypotheses to guide the design of experiments and disambiguate the theories. Artificial intelligence (AI) has dramatically improved state-of-the-art scientific research by helping …
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Enlightening Artificial Intelligence with Science
Today’s artifciail intelligence (AI) systems, while remarkably capable, are largely black boxes. The black-box nature raises concerns for those who build AI – “How can we construct an understand AI in scientifically grounded ways?”, and those who use AI – “How can we trust systems we do not …
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Bridging Deep Learning and Probabilistic Inference: Towards Data Efficiency, Identifiability, and Sampling Scalability
While deep learning has achieved remarkable performance in modelling complex patterns in structured data, a key challenge is its reliance on large datasets. In contrast, probabilistic inference excels in data-scarce settings but suffers from computational inefficiencies for high dimensional data …
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Solving Forward and Inverse Problems for Seismic Imaging using Invertible Neural Networks
Full Waveform Inversion (FWI) is a widely used optimization technique for subsurface imaging where the goal is to estimate the seismic wave velocity beneath the Earth's surface from the observed seismic data at the surface. The problem is primarily governed by the wave equation, which is a …
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Multivariate Time-Series Deep Learning for Short-Term Forecasting of Lost Circulation in Drilling Operations
… interactions among drilling parameters, formation conditions, and operational states. This thesis investigates the use of multivariate time-series deep learning models for short-term lost circulation prediction based on real field drilling data. The dataset used in this study was …
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A dynamic knowledge graph approach to self-driving chemical laboratories
… network. This is due to heterogeneous data formats and resources as an obstacle to holistic integration. This thesis investigates a potential solution to the interoperability problem in chemical experiments by utilising a dynamic knowledge graph to unify the representation of data, software, …