31 citations · 54 across the 5 of their papers we have counts for
6 papers
Scientific Machine Learning-assisted Model Discovery from Telemetry Data
Sebastian Micluta-Campeanu, Avinash Subramanian, Anas Abdelrehim +4
Calibration of dynamic models to data is an important step in building building digital twins of HVAC equipment, thermal loads and control systems. Sometimes, when a model fails to…
Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim
Anas Abdelrehim, Dhairya Gandhi, Sharan Yalburgi +3
Surrogate models are effective tools for accelerated design of complex systems. The result of a design optimization procedure using surrogate models can be used to initialize an op…
Composing Modeling and Simulation with Machine Learning in Julia
Chris Rackauckas, Ranjan Anantharaman, Alan Edelman +10
In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build acce…
ModelingToolkit: A Composable Graph Transformation System For Equation-Based Modeling
Yingbo Ma, Shashi Gowda, Ranjan Anantharaman +3
Getting good performance out of numerical equation solvers requires that the user has provided stable and efficient functions representing their model. However, users should not be…
Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks
Ranjan Anantharaman, Yingbo Ma, Shashi Gowda +4
Modern design, control, and optimization often requires simulation of highly nonlinear models, leading to prohibitive computational costs. These costs can be amortized by evaluatin…
Circuitscape in Julia: High Performance Connectivity Modelling to Support Conservation Decisions
Ranjan Anantharaman, Kimberly Hall, Viral Shah +1
Connectivity across landscapes influences a wide range of conservation-relevant ecological processes, including species movements, gene flow, and the spread of wildfire, pests, and…