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1. Anomaly Detection in Power System State Estimation: Review and New Directions

   https://doi.org/10.3390/en16186678  

   Cooper, Austin; Bretas, Arturo; Meyn, Sean (September 2023, Energies)

   Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest-change detection and artificial intelligence are discussed, and directions for research are suggested with particular emphasis on considerations of the future smart grid. 

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2. Slack reactants: A state-space truncation framework to estimate quantitative behavior of the chemical master equation

   https://doi.org/10.1063/5.0013457  

   Kim, Jinsu; Dark, Jason; Enciso, German; Sindi, Suzanne (August 2020, The Journal of Chemical Physics)

   State space truncation methods are widely used to approximate solutions of the chemical master equation. While most methods of this kind focus on truncating the state space directly, in this work, we propose modifying the underlying chemical reaction network by introducing slack reactants that indirectly truncate the state space. More specifically, slack reactants introduce an expanded chemical reaction network and impose a truncation scheme based on desired mass conservation laws. This network structure also allows us to prove inheritance of special properties of the original model, such as irreducibility and complex balancing. We use the network structure imposed by slack reactants to prove the convergence of the stationary distribution and first arrival times. We then provide examples comparing our method with the stationary finite state projection and finite buffer methods. Our slack reactant system appears to be more robust than some competing methods with respect to calculating first arrival times. 

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3. Model Order Reduction for Water Quality Dynamics

   https://doi.org/10.1029/2021WR029856  

   Wang, Shen; Taha, Ahmad F.; Chakrabarty, Ankush; Sela, Lina; Abokifa, Ahmed A. (April 2022, Water Resources Research)

   Abstract A state‐space representation of water quality (WQ) dynamics describing disinfectant (e.g., chlorine) transport dynamics in drinking water distribution networks has been recently proposed. Such representation is a byproduct of space‐ and time‐discretization of the partial differential equations modeling transport dynamics. This results in a large state‐space dimension even for small networks with tens of nodes. Although such a state‐space model provides a model‐driven approach to predict WQ dynamics, incorporating it into model‐based control algorithms or state estimators for large networks is challenging and at times intractable. To that end, this paper investigates model order reduction (MOR) methods for WQ dynamics with the objective of performing post‐reduction feedback control. The presented investigation focuses on reducing state‐dimension by orders of magnitude, the stability of the MOR methods, and the application of these methods to model predictive control. 

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4. Numerically stable resonating Hartree–Fock

   https://doi.org/10.1063/5.0246790  

   Miller, Ericka Roy; Parker, Shane M (March 2025, The Journal of Chemical Physics)

   The simulation of excited states at low computational cost remains an open challenge for electronic structure (ES) methods. While much attention has been given to orthogonal ES methods, relatively little work has been done to develop nonorthogonal ES methods for excited states, particularly those involving nonorthogonal orbital optimization. We present here a numerically stable formulation of the Resonating Hartree–Fock (ResHF) method that uses the matrix adjugate to remove numerical instabilities arising from nearly orthogonal orbitals, and as a result, we demonstrate improvements to ResHF wavefunction optimization. We then benchmark the performance of ResHF against complete active space self-consistent field in the avoided crossing of LiF, the torsional rotation of ethene, and the singlet–triplet energy gaps of a selection of small molecules. ResHF is a promising excited state method because it incorporates the orbital relaxation of state-specific methods, while retaining the correct state crossings of state-averaged approaches. Our open-source ResHF implementation, yucca, is available on GitLab. 

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5. Unbiased Asymmetric Reinforcement Learning under Partial Observability

   Baisero, Andrea; Amato, Christopher (January 2022, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems)

   In partially observable reinforcement learning, offline training gives access to latent information which is not available during online training and/or execution, such as the system state. Asymmetric actor-critic methods exploit such information by training a history-based policy via a state-based critic. However, many asymmetric methods lack theoretical foundation, and are only evaluated on limited domains. We examine the theory of asymmetric actor-critic methods which use state-based critics, and expose fundamental issues which undermine the validity of a common variant, and limit its ability to address partial observability. We propose an unbiased asymmetric actor-critic variant which is able to exploit state information while remaining theoretically sound, maintaining the validity of the policy gradient theorem, and introducing no bias and relatively low variance into the training process. An empirical evaluation performed on domains which exhibit significant partial observability confirms our analysis, demonstrating that unbiased asymmetric actor-critic converges to better policies and/or faster than symmetric and biased asymmetric baselines. 

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