Quantum Computing in Civil Engineering: Potential Applications and Limitations
Quantum computing has the potential to solve certain computationally challenging problems in civil engineering much faster than traditional approaches, particularly in areas such as simulations, mathematical and machine learning algorithms, and optimization problems.
Feedback Linearizable Discretizations of Second Order Mechanical Systems using Retraction Maps
Utilizing retraction maps to construct feedback linearizable discretizations for second-order mechanical systems.
Multivariable Control of Modular Multilevel Converters with Convergence and Safety Guarantees
Designing a multivariable controller for MMCs ensures safe and efficient operation.
Circuit-centric Genetic Algorithm (CGA) for Analog and Radio-Frequency Circuit Optimization
Optimizing analog/high-frequency circuits using Circuit-centric Genetic Algorithm (CGA) for superior performance.
Fast Real-Time Arbitrary Waveform Generation Using GPUs
Efficiently implementing real-time arbitrary waveform generation using GPUs for various applications.
Control-Coherent Koopman Modeling: A Physical Modeling Approach
Extending Koopman operator theory to non-autonomous control systems without approximation to the input matrix B.
Matrix Analysis Applications in Control Engineering
Matrix analysis is essential for system modeling, stability analysis, controllability, observability, and optimization in control engineering.
PE-GPT: A Physics-Informed Interactive Large Language Model for Power Converter Modulation Design
Large language models like PE-GPT revolutionize power converter modulation design by integrating in-context learning and physics-informed neural networks.
AutoTRIZ: Artificial Ideation with TRIZ and Large Language Models
AutoTRIZ proposes an artificial ideation tool that leverages large language models to automate and enhance the TRIZ methodology for design automation and interpretable innovation.
Compositional Generative Inverse Design: A Paradigm for Complex System Optimization
CinDM introduces a novel approach to compositional generative inverse design, enabling complex system optimization beyond training data.
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