Blueprint for a high-performance fluxonium quantum processor
Transforming stand-alone qubits into a functional, general-purpose quantum processing unit
requires an architecture where many-body quantum entanglement can be generated and …
requires an architecture where many-body quantum entanglement can be generated and …
JuMP 1.0: Recent improvements to a modeling language for mathematical optimization
JuMP is an algebraic modeling language embedded in the Julia programming language.
JuMP allows users to model optimization problems of a variety of kinds, including linear …
JuMP allows users to model optimization problems of a variety of kinds, including linear …
A survey on energy optimization techniques in UAV-based cellular networks: from conventional to machine learning approaches
Wireless communication networks have been witnessing unprecedented demand due to the
increasing number of connected devices and emerging bandwidth-hungry applications …
increasing number of connected devices and emerging bandwidth-hungry applications …
[HTML][HTML] A multi-stage stochastic programming model for the unit commitment of conventional and virtual power plants bidding in the day-ahead and ancillary services …
As more uncontrollable renewable energy sources are present in the power generation
portfolio, the need of more detailed and reliable tools for the optimal operation of energy …
portfolio, the need of more detailed and reliable tools for the optimal operation of energy …
A graph-based methodology for constructing computational models that automates adjoint-based sensitivity analysis
V Gandarillas, AJ Joshy, MZ Sperry, AK Ivanov… - Structural and …, 2024 - Springer
The adjoint method provides an efficient way to compute sensitivities for system models with
a large number of inputs. However, implementing the adjoint method requires significant …
a large number of inputs. However, implementing the adjoint method requires significant …
OMLT: Optimization & machine learning toolkit
The optimization and machine learning toolkit (OMLT) is an open-source software package
incorporating neural network and gradient-boosted tree surrogate models, which have been …
incorporating neural network and gradient-boosted tree surrogate models, which have been …
Oobleck: Resilient distributed training of large models using pipeline templates
Oobleck enables resilient distributed training of large DNN models with guaranteed fault
tolerance. It takes a planning-execution co-design approach, where it first generates a set of …
tolerance. It takes a planning-execution co-design approach, where it first generates a set of …
Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems
Extreme events are featured by high impact and low probability, which can cause severe
damage to power systems. There has been much research focused on resilience-driven …
damage to power systems. There has been much research focused on resilience-driven …
[HTML][HTML] Formulating data-driven surrogate models for process optimization
Recent developments in data science and machine learning have inspired a new wave of
research into data-driven modeling for mathematical optimization of process applications …
research into data-driven modeling for mathematical optimization of process applications …
Optimal locations and sizes of layover charging stations for electric buses
Public transit agencies across the world are rapidly converting their bus fleets from diesel or
hybrid powertrains to battery-electric propulsion systems. To realize the benefits of the …
hybrid powertrains to battery-electric propulsion systems. To realize the benefits of the …