Optimizes energy efficiency in wireless sensor networks with limited information.
problem Maximizing energy efficiency in energy harvesting wireless sensor networks with limited channel state information.
method Modeling as a Multi-Armed Bandits problem and developing an Upper Confidence Bound algorithm.
result Significant gains in energy efficiency compared to benchmark schemes.
Energy savings for DNN inference on resource-constrained devices.
problem Energy efficiency in deep learning inference for constrained devices.
method Efficiently searches through equivalent DNN graphs to find the one with the least execution cost.
result Achieves 24% energy savings with minimal performance impact.
VAV method optimizes learning rate for faster, stable SGD convergence.
problem Optimizing learning rate for efficient and stable machine learning models.
method Energy-based self-adaptive learning rate with auxiliary variable r. result VAV method achieves faster convergence and superior stability with larger learning rates.
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 deep learning model optimizes energy use in buildings.
problem Optimizing energy use and comfort in large buildings.
method Transformer-based metamodel trained with simulation and sensor data, calibrated with CMA-ES, optimized with multi-objective algorithms.
result Optimal settings reduce energy loads while maintaining thermal comfort and air quality.
Deep Neural Networks (DNNs) are increasingly deployed in highly energy-constrained environments such as autonomous drones and wearable devices while at the same time must operate in real-time. Therefore, reducing the energy consumption has become a major design consideration in DNN training. This paper proposes the fir…
Active inference minimizes expected free energy for optimal behavior.
problem Understanding and optimizing behavior in complex systems.
method Combines Bayesian decision theory, optimal Bayesian design, and the free energy principle.
result Active inference emerges as a unified framework for information-seeking, utility maximization, and goal-directed behavior.
Paper optimizes energy trading on DA markets using RL.
problem Volatility and randomness in renewable energy sources.
method Markov Decision Process, reinforcement learning, evolutionary algorithm.
result RL-based strategy generates highest market profits.
Optimal Euclidean structure minimizes energy in weighted toroidal graphs.
problem Finding the optimal Euclidean structure for weighted toroidal graphs.
method Minimizing Dirichlet energy over all possible Euclidean structures and realizations within a fixed homotopy class.
result The optimal Euclidean structure induces a weighted Delaunay decomposition.
Optimizes BESS for cross-market energy arbitrage to boost revenues.
problem Charging and discharging BESS at optimal times to maximize profits.
method Developed a generic framework, backtest engine, and optimization strategy.
result Boosted revenues by 10% through strategic BESS operation.
Optimizes shapes of curves using Möbius energy gradients.
problem Finding optimal shapes of curves within isotopy classes.
method Gradient-based optimization with Sobolev inner products.
result Significantly more efficient and robust optimization methods.
Case study shows impact of co-optimizing energy and reserve for wind energy.
problem Impact of lack of co-optimization of energy and reserve in high wind penetration scenarios.
method Developed two models with and without co-optimization, calibrated with Spanish market parameters.
result Models show significant differences in energy and reserve management.
Current state-of-the-art discrete optimization methods struggle behind when it comes to challenging contrast-enhancing discrete energies (i.e., favoring different labels for neighboring variables). This work suggests a multiscale approach for these challenging problems. Deriving an algebraic representation allows us to…
Optimal energy trading strategy for intraday markets using Hawkes processes.
problem Optimal execution in intraday energy markets with specific trading patterns.
method Calibrated Hawkes process model with transient price impact.
result Substantial cost reductions in TWAP and VWAP benchmarks.
Paper uses optimal transport-based statistics for change point detection.
problem Change point detection in multivariate data.
method Soft rank energy and entropically regularized optimal transport.
result Soft rank energy performs better in real datasets with strong continuity and convergence properties.
New optimization method for sampling from unknown density measures.
problem Sampling from measures with unknown normalization constants.
method Mollified Interaction Energy Descent (MIED) method.
result Gradient flow of MIE converges to chi-square divergence.
AEGD optimizes non-convex functions with dynamic energy updates.
problem Optimizing non-convex functions efficiently and robustly.
method Adaptive Gradient Descent (AEGD) with a dynamically updated energy variable.
result AEGD achieves energy-dependent convergence rates for both convex and non-convex objectives.
In this thesis I explore challenging discrete energy minimization problems that arise mainly in the context of computer vision tasks. This work motivates the use of such "hard-to-optimize" non-submodular functionals, and proposes methods and algorithms to cope with the NP-hardness of their optimization. Consequently, t…
EDCompress optimizes energy efficiency of CNN models on edge devices.
problem Low energy consumption for edge devices with diverse dataflow types.
method Energy-aware model compression using reinforcement learning.
result Improves energy efficiency by 20X, 17X, 37X in various networks.
This paper optimizes a power-to-heat system using reinforcement learning for cost minimization under uncertain conditions.
problem Optimizing a power-to-heat system with fluctuating renewable energy sources.
method Stochastic optimal control, reinforcement learning (Q-learning).
result Reinforcement learning provides an efficient solution to the optimization problem.
Many DNN-enabled vision applications constantly operate under severe energy constraints such as unmanned aerial vehicles, Augmented Reality headsets, and smartphones. Designing DNNs that can meet a stringent energy budget is becoming increasingly important. This paper proposes ECC, a framework that compresses DNNs to m…
Paper extends multivariate rank tests for robust subspace detection.
problem Testing distributional similarity in multivariate data.
method Soft and subspace robust multivariate rank tests based on entropy regularized optimal transport.
result Trade-off between detection power and false alarm rate via projections.
A new AI optimization method uses energy-conserving dynamics inspired by Born-Infeld theory.
problem Optimization challenges in non-convex loss functions and machine learning tasks.
method Discretization of Born-Infeld dynamics for energy-conserving Hamiltonian optimization.
result The method avoids high local minima and outperforms traditional methods in shallow valleys.
Optimal discrete harmonic maps between hyperbolic surfaces are found via minimizing energy.
problem Finding optimal discrete harmonic maps between hyperbolic surfaces.
method Minimizing Dirichlet energy over all possible hyperbolic structures and realizations within a fixed homotopy class.
result At the optimal hyperbolic structure, the discrete harmonic map and edge weights are induced from a weighted Delaunay decomposition.
Gradient estimation techniques applied to programs with randomness in high energy physics.
problem Differentiating programs with discrete randomness in high energy physics.
method Several gradient estimation techniques, including Stochastic AD method, applied to simplified detector design experiments.
result Development of the first fully differentiable branching program.
Paper proposes new loss functions for training energy networks.
problem Challenges in computing gradients for training energy networks.
method Proposes generalized Fenchel-Young losses for efficient gradient computation.
result Demonstrates the calibration of excess risk for linear-concave energies.
Energy markets are strategic to governments and economic development. Several commodities compete as substitutable energy sources and energy diversifiers. Such competition reduces the energy vulnerability of countries as well as portfolios' risk exposure. Vulnerability results mainly from price trends and fluctuations,…
Enhanced tabular benchmarks for energy-efficient neural architecture search.
problem Energy consumption in deep learning models.
method Introducing EC-NAS, an enhanced tabular benchmark with energy consumption data.
result EC-NAS reveals a balance between energy usage and accuracy in neural architecture search.
Tree ensemble method tackles multi-objective constrained optimization in energy systems.
problem Complex, multi-objective, and constrained optimization problems in energy systems.
method Data-driven tree ensemble approach for black-box problems with heterogeneous variable spaces.
result Competitive performance and sampling efficiency compared to state-of-the-art tools.
Lower bounds on geodesic lengths for spheres with Willmore energy.
problem Finding shortest closed geodesics on spheres with Willmore energy.
method Proving a lower bound on geodesic lengths for spheres with Willmore energy below 6π.
result The energy threshold of 6π is optimal and the inequality cannot be extended to higher genus surfaces.
Optimizes energy of mappings from complex projective spaces.
problem Finding energy-minimizing mappings between complex projective spaces and Riemannian manifolds.
method Establishes optimal lower bounds for energy functionals and characterizes optimal mappings.
result Optimal lower bounds for energy functionals are characterized for mappings from real and complex projective spaces.
The paper proves optimal estimates and inequalities for spectral functions on certain manifolds.
problem Optimal estimates and inequalities for spectral functions on weakly 1-complete manifolds.
method Establishes optimal fundamental estimates and weak Morse inequalities for lower energy forms.
result Optimal fundamental estimates and weak Morse inequalities are proven for lower energy forms on weakly 1-complete manifolds.
In [13], a new quasi-local energy is introduced for spacetimes with a non-zero cosmological constant. In this article, we study the small sphere limit of this newly defined quasi-local energy for spacetimes with a negative cosmological constant. For such spacetimes, the anti de-Sitter space is used as the reference for…
Competency questions help experts select best clustering for energy data.
problem Ad hoc and subjective selection of clustering structures by domain experts.
method Formalize expert knowledge and requirements with competency questions.
result Competency questions improve reproducibility and evaluation of clustering applications.
Paper optimizes neural network layers to reduce energy usage without sacrificing accuracy.
problem Energy consumption in deep neural networks during inference.
method Layerwise noise maximization to optimize reliability of memory elements.
result Reduces memory energy consumption by 3.3 times at equal accuracy.
Paper optimizes UAV navigation for IoT data freshness and energy efficiency.
problem Improving data freshness and connectivity for IoT devices with UAVs.
method Deep reinforcement learning model with experience replay for energy-efficient UAV trajectory optimization.
result The proposed approach is 3.6% and 3.13% more energy efficient than greedy and baseline methods.
Physics-informed learning framework for pH systems and EB-PBC control.
problem Control of port-Hamiltonian systems from trajectory data.
method Co-learning of pH system model and EB-PBC through alternating optimization.
result Proven stability and robustness of the learned controller.
Study optimal transport on null hypersurfaces and null energy condition.
problem Optimal transport degeneracy on null hypersurfaces.
method Developed tools to characterize null energy condition using convexity properties of entropy.
result Optimal transport characterization of null energy condition.
Paper proposes a new method to optimize feature coordinates for better image classification.
problem Improving feature extraction for better machine learning classification.
method Mutual-energy inner product optimization method.
result The method enhances low-frequency features and suppresses high-frequency noise, leading to better classification results.
This work optimizes DNN inference for energy-harvesting devices by compressing and selectively executing neural network exits.
problem Inference delays and energy inefficiency in energy-harvesting devices.
method Developed a power trace-aware and exit-guided network compression algorithm for multi-exit neural networks.
result Superior accuracy and reduced latency compared to state-of-the-art techniques.
The free energy functional has recently been proposed as a variational principle for bounded rational decision-making, since it instantiates a natural trade-off between utility gains and information processing costs that can be axiomatically derived. Here we apply the free energy principle to general decision trees tha…
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…
Study optimizes SREC generation and trading in solar energy markets.
problem Optimizing solar energy generation and trading in SREC markets.
method Mean-field game approach to solve stochastic game with heterogeneous agents.
result Characterized firms' optimal controls and equilibrium SREC price.
Optimizes power systems with energy storage under uncertainty using scenario-based method.
problem Optimizing power systems with energy storage, intermittent renewable generation, and uncontrollable loads under uncertainty.
method Developed a novel solution method based on scenario optimization and strategic sampling to solve the chance-constrained optimal power system operation problem.
result The strategic sampling method significantly improves computational efficiency and data-driven convex approximation of power flow.
Paper proposes a novel optimization method for disaggregating smart meter data.
problem Energy disaggregation, inferring appliance-specific energy consumption from aggregate meter data.
method Two-stage optimization approach: first phase uses mixed integer programming, second phase binary quadratic optimization with penalty terms and appliance constraints.
result Proposed method successfully reconstructs appliance signatures, overcoming previous optimization-based methods' limitations.
New algorithm reduces costs in wind energy systems by minimizing decision changes.
problem Costs associated with decision changes in wind energy systems.
method Episodic CBO with movement costs using Gaussian Process and mirror descent.
result Our algorithm consistently outperforms standard CBO in altitude optimization.
In this article, we study the small sphere limit of the Wang-Yau quasi-local energy defined in [18,19]. Given a point p in a spacetime N, we consider a canonical family of surfaces approaching p along its future null cone and evaluate the limit of the Wang-Yau quasi-local energy. The evaluation relies on solving …
A new method combines energy-based models and entropy-regularized optimal transport.
problem Combining energy-based models and optimal transport for generative modeling.
method Energy-guided Entropic Neural Optimal Transport (E-ENT)
result Proves generalization bounds and validates scalability in image translation.