pwc-archive/papers-with-abstracts huggingface dataset:
Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate
Authors: Matthew Bossart, Jose Daniel Lara, Ciaran Roberts, Rodrigo Henriquez-Auba, Duncan Callaway, Bri-Mathias Hodge
Date: 2024-05-10 00:00:00

The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. In this paper, we propose a data-driven surrogate model based on implicit machine learning -- specifically deep equilibrium layers and neural ordinary differential equations -- to learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.
Coherent Design of Wind Turbine Controllers Considering Transitions between Operating Regions using Fuzzy Membership Functions
Authors: Horst Schulte
Date: 2024-05-10 00:00:00
Tasks: Pitch control, valid

This paper presents a coherent design of wind turbine controllers with explicit consideration of transitions between operating regions by fuzzy membership functions. In improving the design process of wind turbines, the transitions between partial-load operation by torque control and full-load operation by pitch control need to be systematically considered. From the first view, fuzzy methods for blending separately designed control laws are an obvious choice. However, valid design rules must be developed to ensure stability and performance during the transition. A model-based control design procedure in the Takagi-Sugeno fuzzy framework using the sector nonlinearity method is proposed to achieve the above control design objectives. In addition to a detailed mathematical analysis of the design, the method's applicability is verified by simulation studies using a high-fidelity reference wind turbine model.
Site-dependent Solutions of Wave Energy Converter Farms with Surrogate Models, Control Co-design, and Layout Optimization
Authors: Saeed Azad, Daniel R. Herber, Suraj Khanal, Gaofeng Jia
Date: 2024-05-10 00:00:00

Design of wave energy converter farms entails multiple domains that are coupled, and thus, their concurrent representation and consideration in early-stage design optimization has the potential to offer new insights and promising solutions with improved performance. Concurrent optimization of physical attributes (e.g., plant) and the control system design is often known as control co-design or CCD. To further improve performance, the layout of the farm must be carefully optimized in order to ensure that constructive effects from hydrodynamic interactions are leveraged, while destructive effects are avoided. The variations in the joint probability distribution of waves, stemming from distinct site locations, affect the farm's performance and can potentially influence decisions regarding optimal plant selection, control strategies, and layout configurations. Therefore, this paper undertakes a concurrent exploration of control co-design and layout optimization for a farm comprising five devices, modeled as heaving cylinders in the frequency domain, situated across four distinct site locations: Alaskan Coasts, East Coast, Pacific Islands, and West Coast. The challenge of efficiently and accurately estimating hydrodynamic coefficients within the optimization loop was mitigated through the application of surrogate modeling and many-body expansion principles. Results indicate the optimized solutions exhibit variations in plant, control, and layout for each candidate site, signifying the importance of system-level design with environmental considerations from the early stages of the design process.
Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies
Authors: Kyle Dunlap, Nathaniel Hamilton, Francisco Viramontes, Derrek Landauer, Evan Kain, Kerianne L. Hobbs
Date: 2024-05-10 00:00:00
Tasks: Reinforcement Learning (RL)

As the number of spacecraft on orbit continues to grow, it is challenging for human operators to constantly monitor and plan for all missions. Autonomous control methods such as reinforcement learning (RL) have the power to solve complex tasks while reducing the need for constant operator intervention. By combining RL solutions with run time assurance (RTA), safety of these systems can be assured in real time. However, in order to use these algorithms on board a spacecraft, they must be able to run in real time on space grade processors, which are typically outdated and less capable than state-of-the-art equipment. In this paper, multiple RL-trained neural network controllers (NNCs) and RTA algorithms were tested on commercial-off-the-shelf (COTS) and radiation tolerant processors. The results show that all NNCs and most RTA algorithms can compute optimal and safe actions in well under 1 second with room for further optimization before deploying in the real world.
Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles
Authors: Kyle Dunlap, Nathaniel Hamilton, Zachary Lippay, Matthew Shubert, Sean Phillips, Kerianne L. Hobbs
Date: 2024-05-10 00:00:00

On-orbit spacecraft inspection is an important capability for enabling servicing and manufacturing missions and extending the life of spacecraft. However, as space operations become increasingly more common and complex, autonomous control methods are needed to reduce the burden on operators to individually monitor each mission. In order for autonomous control methods to be used in space, they must exhibit safe behavior that demonstrates robustness to real world disturbances and uncertainty. In this paper, neural network controllers (NNCs) trained with reinforcement learning are used to solve an inspection task, which is a foundational capability for servicing missions. Run time assurance (RTA) is used to assure safety of the NNC in real time, enforcing several different constraints on position and velocity. The NNC and RTA are tested in the real world using unmanned aerial vehicles designed to emulate spacecraft dynamics. The results show this emulation is a useful demonstration of the capability of the NNC and RTA, and the algorithms demonstrate robustness to real world disturbances.
Dynamic Optimization of Proton Exchange Membrane Water Electrolyzers Considering Usage-Based Degradation
Authors: Landon Schofield, Benjamin Paren, Ruaridh Macdonald, Yang Shao-Horn, Dharik Mallapragada
Date: 2024-05-10 00:00:00

We present a techno-economic optimization model for evaluating the design and operation of proton exchange membrane (PEM) electrolyzers, crucial for hydrogen production powered by variable renewable electricity. This model integrates a 0-D physics representation of the electrolyzer stack, complete mass and energy balances, operational constraints, and empirical data on use-dependent degradation. Utilizing a decomposition approach, the model predicts optimal electrolyzer size, operation, and necessary hydrogen storage to satisfy baseload demands across various technology and electricity price scenarios. Analysis for 2022 shows that including degradation effects raises the levelized cost of hydrogen from \$4.56/kg to \$6.60/kg and decreases stack life to two years. However, projections for 2030 anticipate a significant reduction in costs to approximately \$2.50/kg due to lower capital expenses, leading to larger stacks, extended lifetimes, and less hydrogen storage. This approach is adaptable to other electrochemical systems relevant for decarbonization.
Region of Attraction Estimation for Free-Floating Systems under Time-Varying LQR Control
Authors: Lasse Shala, Shubham Vyas, Mohamed Khalil Ben-Larbi, Shivesh Kumar, Enrico Stoll
Date: 2024-05-10 00:00:00

Future Active Debris Removal (ADR) and On Orbit Servicing (OOS) missions demand for elaborate closed loop controllers. Feasible control architectures should take into consideration the inherent coupling of the free floating dynamics and the kinematics of the system. Recently, Time-Varying Linear Quadratic Regulators (TVLQR) have been used to stabilize underactuated systems that exhibit a similar kinodynamic coupling. Furthermore, this control approach integrates synergistically with Lyapunov based region of attraction (ROA) estimation, which, in the context of ADR and OOS, allows for reasoning about composability of different sub-maneuvers. In this paper, TVLQR was used to stabilize an ADR detumbling maneuver in simulation. Moreover, the ROA of the closed loop dynamics was estimated using a probabilistic method. In order to demonstrate the real-world applicability for free floating robots, further experiments were conducted onboard a free floating testbed.
Why Decussate? Topological Constraints on 3D Wiring
Authors: Troy Shinbrot, Wise Young
Date: 2024-05-13 00:00:00

Many vertebrate motor and sensory systems decussate, or cross the midline to the opposite side of the body. The successful crossing of millions of axons during development requires a complex of tightly controlled regulatory processes. Because these processes have evolved in many distinct systems and organisms, it seems reasonable to presume that decussation confers a significant functional advantage. Yet if this is so, the nature of this advantage is not understood. In this article, we examine constraints imposed by topology on the ways that a three-dimensional processor and environment can be wired together in a continuous, somatotopic, way. We show that as the number of wiring connections grows, decussated arrangements become overwhelmingly more robust against wiring errors than seemingly simpler same-sided wiring schemes. These results provide a predictive approach for understanding how 3D networks must be wired if they are to be robust, and therefore have implications both for future large-scale computational networks and for complex bio-medical devices
Double Robustness of Local Projections and Some Unpleasant VARithmetic
Authors: José Luis Montiel Olea, Mikkel Plagborg-Møller, Eric Qian, Christian K. Wolf
Date: 2024-05-15 00:00:00

We consider impulse response inference in a locally misspecified vector autoregression (VAR) model. The conventional local projection (LP) confidence interval has correct coverage even when the misspecification is so large that it can be detected with probability approaching 1. This result follows from a "double robustness" property analogous to that of popular partially linear regression estimators. In contrast, the conventional VAR confidence interval with short-to-moderate lag length can severely undercover, even for misspecification that is small, economically plausible, and difficult to detect statistically. There is no free lunch: the VAR confidence interval has robust coverage only if the lag length is so large that the interval is as wide as the LP interval.
Identifying Heterogeneous Decision Rules From Choices When Menus Are Unobserved
Authors: Larry G Epstein, Kaushil Patel
Date: 2024-05-15 00:00:00

Given only aggregate choice data and limited information about how menus are distributed across the population, we describe what can be inferred robustly about the distribution of preferences (or more general decision rules). We strengthen and generalize existing results on such identification and provide an alternative analytical approach to study the problem. We show further that our model and results are applicable, after suitable reinterpretation, to other contexts. One application is to the robust identification of the distribution of updating rules given only the population distribution of beliefs and limited information about heterogeneous information sources.
Variational Bayes and non-Bayesian Updating
Authors: Tomasz Strzalecki
Date: 2024-05-14 00:00:00

I show how variational Bayes can be used as a microfoundation for a popular model of non-Bayesian updating.
Latent group structure in linear panel data models with endogenous regressors
Authors: JunHo Choi, Ryo Okui
Date: 2024-05-14 00:00:00
Tasks: regression

This paper concerns the estimation of linear panel data models with endogenous regressors and a latent group structure in the coefficients. We consider instrumental variables estimation of the group-specific coefficient vector. We show that direct application of the Kmeans algorithm to the generalized method of moments objective function does not yield unique estimates. We newly develop and theoretically justify two-stage estimation methods that apply the Kmeans algorithm to a regression of the dependent variable on predicted values of the endogenous regressors. The results of Monte Carlo simulations demonstrate that two-stage estimation with the first stage modeled using a latent group structure achieves good classification accuracy, even if the true first-stage regression is fully heterogeneous. We apply our estimation methods to revisiting the relationship between income and democracy.
Random Utility Models with Skewed Random Components: the Smallest versus Largest Extreme Value Distribution
Authors: Richard T. Carson, Derrick H. Sun, Yixiao Sun
Date: 2024-05-13 00:00:00

At the core of most random utility models (RUMs) is an individual agent with a random utility component following a largest extreme value Type I (LEVI) distribution. What if, instead, the random component follows its mirror image -- the smallest extreme value Type I (SEVI) distribution? Differences between these specifications, closely tied to the random component's skewness, can be quite profound. For the same preference parameters, the two RUMs, equivalent with only two choice alternatives, diverge progressively as the number of alternatives increases, resulting in substantially different estimates and predictions for key measures, such as elasticities and market shares. The LEVI model imposes the well-known independence-of-irrelevant-alternatives property, while SEVI does not. Instead, the SEVI choice probability for a particular option involves enumerating all subsets that contain this option. The SEVI model, though more complex to estimate, is shown to have computationally tractable closed-form choice probabilities. Much of the paper delves into explicating the properties of the SEVI model and exploring implications of the random component's skewness. Conceptually, the difference between the LEVI and SEVI models centers on whether information, known only to the agent, is more likely to increase or decrease the systematic utility parameterized using observed attributes. LEVI does the former; SEVI the latter. An immediate implication is that if choice is characterized by SEVI random components, then the observed choice is more likely to correspond to the systematic-utility-maximizing choice than if characterized by LEVI. Examining standard empirical examples from different applied areas, we find that the SEVI model outperforms the LEVI model, suggesting the relevance of its inclusion in applied researchers' toolkits.
Robust Estimation and Inference for High-Dimensional Panel Data Models
Authors: Jiti Gao, Fei Liu, Bin Peng, Yayi Yan
Date: 2024-05-13 00:00:00

This paper provides the relevant literature with a complete toolkit for conducting robust estimation and inference about the parameters of interest involved in a high-dimensional panel data framework. Specifically, (1) we allow for non-Gaussian, serially and cross-sectionally correlated and heteroskedastic error processes, (2) we develop an estimation method for high-dimensional long-run covariance matrix using a thresholded estimator, (3) we also allow for the number of regressors to grow faster than the sample size. Methodologically and technically, we develop two Nagaev--types of concentration inequalities: one for a partial sum and the other for a quadratic form, subject to a set of easily verifiable conditions. Leveraging these two inequalities, we derive a non-asymptotic bound for the LASSO estimator, achieve asymptotic normality via the node-wise LASSO regression, and establish a sharp convergence rate for the thresholded heteroskedasticity and autocorrelation consistent (HAC) estimator. We demonstrate the practical relevance of these theoretical results by investigating a high-dimensional panel data model with interactive effects. Moreover, we conduct extensive numerical studies using simulated and real data examples.
Kernel Three Pass Regression Filter
Authors: Rajveer Jat, Daanish Padha
Date: 2024-05-12 00:00:00
Tasks: regression

We forecast a single time series using a high-dimensional set of predictors. When these predictors share common underlying dynamics, an approximate latent factor model provides a powerful characterization of their co-movements Bai(2003). These latent factors succinctly summarize the data and can also be used for prediction, alleviating the curse of dimensionality in high-dimensional prediction exercises, see Stock & Watson (2002a). However, forecasting using these latent factors suffers from two potential drawbacks. First, not all pervasive factors among the set of predictors may be relevant, and using all of them can lead to inefficient forecasts. The second shortcoming is the assumption of linear dependence of predictors on the underlying factors. The first issue can be addressed by using some form of supervision, which leads to the omission of irrelevant information. One example is the three-pass regression filter proposed by Kelly & Pruitt (2015). We extend their framework to cases where the form of dependence might be nonlinear by developing a new estimator, which we refer to as the Kernel Three-Pass Regression Filter (K3PRF). This alleviates the aforementioned second shortcoming. The estimator is computationally efficient and performs well empirically. The short-term performance matches or exceeds that of established models, while the long-term performance shows significant improvement.
On the Ollivier-Ricci curvature as fragility indicator of the stock markets
Authors: Joaquín Sánchez García, Sebastian Gherghe
Date: 2024-05-12 00:00:00

Recently, an indicator for stock market fragility and crash size in terms of the Ollivier-Ricci curvature has been proposed. We study analytical and empirical properties of such indicator, test its elasticity with respect to different parameters and provide heuristics for the parameters involved. We show when and how the indicator accurately describes a financial crisis. We also propose an alternate method for calculating the indicator using a specific sub-graph with special curvature properties.
Identifying Peer Effects in Networks with Unobserved Effort and Isolated Students
Authors: Aristide Houndetoungan, Cristelle Kouame, Michael Vlassopoulos
Date: 2024-05-10 00:00:00

Peer influence on effort devoted to some activity is often studied using proxy variables when actual effort is unobserved. For instance, in education, academic effort is often proxied by GPA. We propose an alternative approach that circumvents this approximation. Our framework distinguishes unobserved shocks to GPA that do not affect effort from preference shocks that do affect effort levels. We show that peer effects estimates obtained using our approach can differ significantly from classical estimates (where effort is approximated) if the network includes isolated students. Applying our approach to data on high school students in the United States, we find that peer effect estimates relying on GPA as a proxy for effort are 40% lower than those obtained using our approach.
Generalization Issues in Conjoint Experiment: Attention and Salience
Authors: Jiawei Fu, Xiaojun Li
Date: 2024-05-10 00:00:00
Tasks: Experimental Design

Can the causal effects estimated in an experiment be generalized to real-world scenarios? This question lies at the heart of social science studies. External validity primarily assesses whether experimental effects persist across different settings, implicitly presuming the consistency of experimental effects with their real-life counterparts. However, we argue that this presumed consistency may not always hold, especially in experiments involving multi-dimensional decision processes, such as conjoint experiments. We introduce a formal model to elucidate how attention and salience effects lead to three types of inconsistencies between experimental findings and real-world phenomena: amplified effect magnitude, effect sign reversal, and effect importance reversal. We derive testable hypotheses from each theoretical outcome and test these hypotheses using data from various existing conjoint experiments and our own experiments. Drawing on our theoretical framework, we propose several recommendations for experimental design aimed at enhancing the generalizability of survey experiment findings.
Exploring the Potential of Large Language Models for Automation in Technical Customer Service
Authors: Jochen Wulf, Juerg Meierhofer
Date: 2024-05-15 00:00:00
Tasks: Management, RAG, Retrieval-augmented Generation

Purpose: The purpose of this study is to investigate the potential of Large Language Models (LLMs) in transforming technical customer service (TCS) through the automation of cognitive tasks. Design/Methodology/Approach: Using a prototyping approach, the research assesses the feasibility of automating cognitive tasks in TCS with LLMs, employing real-world technical incident data from a Swiss telecommunications operator. Findings: Lower-level cognitive tasks such as translation, summarization, and content generation can be effectively automated with LLMs like GPT-4, while higher-level tasks such as reasoning require more advanced technological approaches such as Retrieval-Augmented Generation (RAG) or finetuning ; furthermore, the study underscores the significance of data ecosystems in enabling more complex cognitive tasks by fostering data sharing among various actors involved. Originality/Value: This study contributes to the emerging theory on LLM potential and technical feasibility in service management, providing concrete insights for operators of TCS units and highlighting the need for further research to address limitations and validate the applicability of LLMs across different domains.