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Found 10 papers, 6 papers with code
Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks
We propose a scalable framework for the learning of high-dimensional parametric maps via adaptively constructed residual network (ResNet) maps between reduced bases of the inputs and outputs.
Reduced operator inference for nonlinear partial differential equations
First, ideas from projection-based model reduction are used to explicitly parametrize the learned model by low-dimensional polynomial operators which reflect the known form of the governing PDE.
Certifiable Risk-Based Engineering Design Optimization
CRiBDO is contrasted with reliability-based design optimization (RBDO), where uncertainties are accounted for via the probability of failure, through a structural and a thermal design problem.
Optimization and Control Computational Engineering, Finance, and Science Data Analysis, Statistics and Probability Computation
Data-driven reduced-order models via regularized operator inference for a single-injector combustion process
With appropriate regularization and an informed selection of learning variables, the reduced-order models exhibit high accuracy in re-predicting the training regime and acceptable accuracy in predicting future dynamics, while achieving close to a million times speedup in computational cost.
Computational Engineering, Finance, and Science J.2
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
The proposed method learns operators for the linear and polynomially nonlinear dynamics via a least-squares problem, where the given non-polynomial terms are incorporated in the right-hand side.
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
The lifting map is applied to data obtained by evaluating a model for the original nonlinear system.
BIG-bench Machine Learning
Physics-informed machine learning
Learning physics-based reduced-order models for a single-injector combustion process
The machine learning perspective brings the flexibility to use transformed physical variables to define the POD basis.
Contour location via entropy reduction leveraging multiple information sources
We introduce an algorithm to locate contours of functions that are expensive to evaluate.
Lookahead Bayesian Optimization with Inequality Constraints
We consider the task of optimizing an objective function subject to inequality constraints when both the objective and the constraints are expensive to evaluate.
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach
We consider the problem of optimizing an expensive objective function when a finite budget of total evaluations is prescribed.
