The recent developments in the research area of synthetic biology and related fields indicate a significant shift towards more sophisticated and integrated modeling and control strategies. There is a growing emphasis on leveraging advanced mathematical tools, such as differential geometry and port-Hamiltonian neural networks, to analyze and optimize complex biological and energy systems. These tools are enabling the development of more accurate and efficient models, particularly for nonlinear systems, which are prevalent in synthetic biology circuits and power grids. Additionally, there is a notable trend towards the integration of data-driven approaches with formal guarantees, which is facilitating the control of large-scale networks with unknown models and topologies. This approach not only enhances the robustness of control strategies but also reduces computational complexity, making it feasible to apply these methods to real-world systems. Furthermore, the field is witnessing innovative frameworks for energy storage and trading, which are crucial for balancing renewable energy sources and ensuring grid stability. These frameworks often incorporate game-theoretic models and privacy-preserving algorithms to address the challenges of peer-to-peer energy trading and data security. Overall, the advancements are pushing the boundaries of what is possible in terms of system stability, efficiency, and resilience, with a strong focus on practical applications and real-time control.
Advanced Modeling and Control Strategies in Synthetic Biology and Energy Systems
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Analysing control-theoretic properties of nonlinear synthetic biology circuits
Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
A capacity renting framework for shared energy storage considering peer-to-peer energy trading of prosumers with privacy protection
Stability Analysis of Distributed Estimators for Large-Scale Interconnected Systems: Time-Varying and Time-Invariant Cases
Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting
Data-Driven Control of Large-Scale Networks with Formal Guarantees: A Small-Gain Free Approach
Modeling and Detection of Critical Slowing Down in Epileptic Dynamics
Co-Scheduling of Energy and Production in Discrete Manufacturing Considering Decision-Dependent Uncertainties
Two-Stage Stochastic Optimization for Low-Carbon Dispatch in a Combined Energy System
Spike Talk in Power Electronic Grids -- Leveraging Post Moore's Computing Laws
Effects of charging and discharging capabilities on trade-offs between model accuracy and computational efficiency in pumped thermal electricity storage
Neural Network Certification Informed Power System Transient Stability Preventive Control with Renewable Energy
Robust performance for switched systems with constrained switching and its application to weakly hard real-time control systems
Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach
Accelerating Quasi-Static Time Series Simulations with Foundation Models
A Comparative Analysis of Electricity Consumption Flexibility in Different Industrial Plant Configurations
Experimental Demonstration of Remote Synchronization in Coupled Nonlinear Oscillator
A small-gain criterion for 2-contraction of large scale interconnected systems