A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Physics-informed learning framework for pH systems and EB-PBC control.
Neural ODEs control graph dynamics with low energy feedback.
An energy based approach for stabilizing a mechanical system has offered a simple yet powerful control scheme. However, since it does not impose such strong constraints on parameter space of the controller, finding appropriate parameter values for an optimal controller is known to be hard. This paper intends to generat…
Controller seeks informative system observations to predict nonlinear dynamics.
Tripod spiders' energy control analyzed for Hooke and Coulomb potentials.
Paper designs energy-based controllers and observers for complex systems.
Improved diffusion models using energy distillation and sequential Monte Carlo.
This paper optimizes a power-to-heat system using reinforcement learning for cost minimization under uncertain conditions.
Develops control and observer methods for complex systems.
RNE provides a flexible framework for diffusion models, enabling inference-time control and energy-based training.
Dissipative SymODEN learns dynamics with dissipation and control from data.
The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine lear…
EnCF improves data assimilation for implicit, non-smooth observations.
Study optimal incentives for cleaner energy production.
This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting). Achieving their fast travel and low motion energy without inter-UAV collision under wind perturbation is a daunting c…
Derives time-averaged active inference from control principles.
Improved text generation with constraints using discrete auto-regressive biasing.
In this paper, we consider nonlinear PDEs in a port-Hamiltonian setting based on an underlying jet-bundle structure. We restrict ourselves to systems with 1-dimensional spatial domain and 2nd-order Hamiltonian including certain dissipation models that can be incorporated in the port- Hamiltonian framework by means of a…
Bayesian optimization helps learn optimal controls for nuclear fusion reactions.
This work develops a novel power control framework for energy-efficient power control in wireless networks. The proposed method is a new branch-and-bound procedure based on problem-specific bounds for energy-efficiency maximization that allow for faster convergence. This enables to find the global solution for all of t…
Software Defined Networking (SDN) achieves programmability of a network through separation of the control and data planes. It enables flexibility in network management and control. Energy efficiency is one of the challenging global problems which has both economic and environmental impact. A massive amount of informati…
New algorithm controls large groups of devices to match energy demand signals.
We give a singular control approach to the problem of minimizing an energy functional for measures with given total mass on a compact real interval, when energy is defined in terms of a completely monotone kernel. This problem occurs both in potential theory and when looking for optimal financial order execution strate…
We explore a new method for discrete-time control problems using randomization and entropy.
In this paper we consider an energy storage optimization problem in finite time in a model with partial information that allows for a changing economic environment. The state process consists of the storage level controlled by the storage manager and the energy price process, which is a diffusion process the drift of w…
Agents learn and control complex mechanical systems through shared memories.
The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energy-based model, where the observed expert trajectori…
We study various aspects related to boundary regularity of complete properly embedded Willmore surfaces in H3, particularly those related to assumptions on boundedness or smallness of a certain weighted version of the Willmore energy. We prove, in particular, that small energy controls C1 boundary regularity. We examin…
Traditional centralized energy systems have the disadvantages of difficult management and insufficient incentives. Blockchain is an emerging technology, which can be utilized in energy systems to enhance their management and control. Integrating token economy and blockchain technology, token economic systems in energy …
Distributed, controllable energy storage devices offer several benefits to electric power system operation. Three such benefits include reducing peak load, providing standby power, and enhancing power quality. These benefits, however, are only realized during peak load or during an outage, events that are infrequent. T…
New method controls renewable energy storage and portfolio selection with probabilistic constraints.
In this letter, we address the problem of controlling energy storage systems (ESSs) for arbitrage in real-time electricity markets under price uncertainty. We first formulate this problem as a Markov decision process, and then develop a deep reinforcement learning based algorithm to learn a stochastic control policy th…
The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require extensive communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn c…
Reinforcement learning for continuous-time risk-sensitive asset allocation
In this paper we prove a convergence result for sequences of Willmore immersions with simple minimal bubbles. To this end we replace the total curvature control in T. Rivière's proof of the -regularity for Willmore immersions by a control of the local Willmore energy.
FEAT estimates free energy using adaptive transports.
For a sequence of coupled fields from a compact Riemann surface with smooth boundary to a general compact Riemannian manifold with uniformly bounded energy and satisfying the Dirac-harmonic system up to some uniformly controlled error terms, we show that the energy identity holds during a blow-up pr…
The paper develops methods to reduce deployment risk under dynamic covariate shifts.
Gradient flows for knot energies ensure long-term existence of knotted loops.
We tune one of the most common heating, ventilation, and air conditioning (HVAC) control loops, namely the temperature control of a room. For economical and environmental reasons, it is of prime importance to optimize the performance of this system. Buildings account from 20 to 40% of a country energy consumption, and …
New proof of harmonic map uniqueness with analytic targets.
Quantitative stability for nearly minimizing Yamabe metrics.
We introduce a data-based approach to estimating key quantities which arise in the study of nonlinear control systems and random nonlinear dynamical systems. Our approach hinges on the observation that much of the existing linear theory may be readily extended to nonlinear systems - with a reasonable expectation of suc…
Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.
Study optimizes SREC generation and trading in solar energy markets.
The Willmore energy of a closed surface in R^n is the integral of its squared mean curvature, and is invariant uner Möbius transformations of R^n. We show that any torus in R^3 with energy at most has a representative under the Möbius action, for which the induced metric and a conformal metric of constant (…
Develops ECD framework for optimizing machine learning problems.
MF-PID uses interacting samples to efficiently transport probability mass.