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Chaudhuri, Anirban

Publications and source records attributed to Chaudhuri, Anirban.

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

GP-BayesOpInf

SAND2025-01851O GP-BayesOpInf is a software tool that uses algorithms to combine Gaussian process regression, principal component analysis, and linear Bayesian inference to produce a probabilistic reduced-order model for time-dependent systems. Numerical examples include the compressible Euler equations for an ideal gas, a heat diffusion process with a nonlinear reaction term, and a set of ordinary differential equations describing a compartmental model in epidemiology. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2022

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. When just few training data are available, it is beneficial to have a compact parametrization in order to ameliorate the ill-posedness of the neural network training problem. By linearly restricting high-dimensional maps to informed reduced bases of the inputs, one can compress high-dimensional maps in a constructive way that can be used to detect appropriate basis ranks, equipped with rigorous error estimates. A scalable neural network learning framework is thus to learn the nonlinear compressed reduced basis mapping. Unlike the reduced basis construction, however, neural network constructions are not guaranteed to reduce errors by adding representation power, making it difficult to achieve good practical performance. Inspired by recent approximation theory that connects ResNets to sequential minimizing flows, we present an adaptive ResNet construction algorithm. This algorithm allows for depth-wise enrichment of the neural network approximation, in a manner that can achieve good practical performance by first training a shallow network and then adapting. We prove universal approximation of the associated neural network class for $L^2_v$ functions on compact sets. Our overall framework allows for constructive means to detect appropriate breadth and depth, and related compact parametrizations of neural networks, significantly reducing the need for architectural hyperparameter tuning. Numerical experiments for parametric PDE problems and a 3D CFD wing design optimization parametric map demonstrate that the proposed methodology can achieve remarkably high accuracy for limited training data, and outperformed other neural network strategies we compared against.

42 ENGINEERING↗
DOE OSTI2022

Noninvasive acoustical property measurement of fluids

Methods for noninvasive determination of acoustical properties of flowing in pipes having a large ratio (>10) of pipe diameter to wall thickness, and in highly attenuating fluids are described. When vibrations are excited on the outer surface of the wall of a pipe, the resulting vibrations propagate directly through the wall in a normal direction and through the pipe wall as guided waves, appearing on the opposite side of the pipe. This dual path propagation through pipes, where guided waves take the circumferential path in the wall of the pipe and may interfere with the time of-flight measurement obtained from the direct path through the fluid, is at least in part resolved by subtracting the signal from the guided wave from the combined signal, thereby permitting improved observation of the direct path propagation through the fluid.

Sinha, Dipen N.↗
DOE OSTI2022

Process window estimation in manufacturing through Entropy-Sigma active learning

In manufacturing, there exist boundary identification problems for defining parameter spaces that meet desired thresholds on outcomes. This paper presents an Entropy-Sigma acquisition function for active learning of the process window/map in manufacturing using a Gaussian Process surrogate. Here, the method is applied to identify the stability boundary for the stability process map in machining using time-domain simulations with a periodic sampling stability metric. Results show that the proposed Entropy-Sigma method significantly outperforms Latin hypercube sampling or grid-based methods. The described method can be applied to identify the process window/map for any manufacturing application using a quantitative process outcome metric.

42 ENGINEERING↗
DOE OSTI2022

Bayesian optimization for inverse calibration of expensive computer models: A case study for Johnson-Cook model in machining

Inverse model calibration for identifying the constitutive model parameters can be computationally demanding for expensive-to-evaluate simulation models. Here, this paper presents a modified Bayesian optimization (BO) method, denoted as BO-bound, that incorporates theoretical bounds on the quantity of interest. A case study for the inverse calibration of the Johnson Cook (J-C) flow stress model parameters is presented using machining (cutting) force data. The results show fast calibration of the five J-C parameters within 25 simulations. In general, the BO-bound method is applicable for inverse calibration of any expensive simulation models as well as optimization problems with known bounds.

Bayesian optimization↗
DOE OSTI2020

Simultaneous real-time measurement of composition, flow, attenuation, density, and pipe-wallthickness in multiphase fluids

Apparatus (10) and methods for making simultaneous measurements of composition (water-cut), fluid flow, and sound attenuation in a multiphase fluid flowing (15) through a pipe (12) in real-time, using the same apparatus (10) are described. Additionally, the apparatus (10) provides real-time pipe wall thickness monitoring for observing pipe corrosion or internal deposition. Knowledge of wall thickness is necessary to correct for water-cut (oil-water composition) automatically by adjusting the liquid path length internal to the pipe (spool). The use of short duration frequency chirp excitation signals (24) enables the apparatus to provide information that can be used to extract multiple levels of information from the same measurement in multiphase fluids including the presence of a significant quantity of gas (˜60% gas volume fraction) in different flow regimes. Besides measuring steady flow, this device is useful for measurements during fast changing flows, such as for a rod-pumped well. Measurements up to about 1000 times a second can reliably be made.

Sinha, Dipen N.↗
DOE OSTI2018

Methods for measuring properties of multiphase oil-water-gas mixtures

Methods for real-time, continuous measurements of the composition and other properties of individual phases of petroleum, water and gas mixtures during the oil production process, without requiring test separators, test lines, with associated valving and instrumentation, are described. Embodiments of the present invention direct ultrasonic sound transmission through a flowing multiphase fluid in three frequency ranges: low frequencies, gas bubble resonance frequencies, and high frequencies, wherein certain sound propagation measurements, including sound speed, sound attenuation and sound scattering, are made in one or more of the three separate frequency regions, from which the multiphase composition and other properties are extracted without having to separate the multiphase fluid or the gas from the flowing stream.

02 PETROLEUM↗