tianyis paper

Author: sbryngelson

_data/news.yml

@@ -1,3 +1,6 @@
+- date: May 1, 2026
+  headline: "Postdoc Dr. Tianyi Chu's <a href='https://arxiv.org/abs/2505.13283' target='_blank' rel='noopener noreferrer'>paper</a> on accelerating Bayesian optimal experimental design for soft material characterization is accepted to the <i>Journal of Computational Physics</i>. Collaboration with Jon Estrada's group."
+
 - date: April 29, 2026
   headline: "Spencer is leading a breakout discussion at the <a href='https://www.olcf.ornl.gov/calendar/userconcall-apr2026/' target='_blank' rel='noopener noreferrer'>OLCF User Town Hall</a>, hosted by the <a href='https://www.olcf.ornl.gov/community/oug/' target='_blank' rel='noopener noreferrer'>OLCF User Group Executive Board</a>."
 

cv/cv.pdf

@@ -76,13 +76,13 @@ @unpublished{Jawetz25
 abstract = {Heat transfer involving phase change is computationally intensive due to moving phase boundaries, nonlinear computations, and time step restrictions. This paper presents a quantum lattice Boltzmann method (QLBM) for simulating heat transfer with phase change. The approach leverages the statistical nature of the lattice Boltzmann method (LBM) while addressing the challenges of nonlinear phase transitions in quantum computing. The method implements an interface-tracking strategy that partitions the problem into separate solid and liquid domains, enabling the algorithm to handle the discontinuity in the enthalpy-temperature relationship. We store phase change information in the quantum circuit to avoid frequent information exchange between classical and quantum hardware, a bottleneck in many quantum applications. Results from the implementation agree with both classical LBM and analytical solutions, demonstrating QLBM as an effective approach for analyzing thermal systems with phase transitions. Simulations using 17 lattice nodes with 51 qubits demonstrate root-mean-square (RMS) errors below 0.005 when compared against classical solutions. The method accurately tracks interface movement during phase transition.},
 }
 
-@unpublished{Chu25b,
+@article{Chu25b,
 Author = {T. Chu and J. B Estrada and S H. Bryngelson},
 Title = {Accelerating {B}ayesian optimal experimental design via local radial basis functions: {A}pplication to soft material characterization},
-note = {arXiv:2505.13283},
-file = {chu-rbf-arxiv-25.pdf},
-arxiv = {arXiv.2505.13283},
-year = {2025},
+note = {in press},
+file = {chu-jcp-26.pdf},
+year = {2026},
+journal = {Journal of Computational Physics},
 doi = {10.48550/arXiv.2505.13283},
 abstract = {We develop a computational approach that significantly improves the efficiency of Bayesian optimal experimental design (BOED) using local radial basis functions (RBFs). The presented RBF-BOED method uses the intrinsic ability of RBFs to handle scattered parameter points, a property that aligns naturally with the probabilistic sampling inherent in Bayesian methods. By constructing accurate deterministic surrogates from local neighborhood information, the method enables high-order approximations with reduced computational overhead. As a result, computing the expected information gain (EIG) requires evaluating only a small uniformly sampled subset of prior parameter values, greatly reducing the number of expensive forward-model simulations needed. For demonstration, we apply RBF-BOED to optimize a laser-induced cavitation (LIC) experimental setup, where forward simulations follow from inertial microcavitation rheometry (IMR) and characterize the viscoelastic properties of hydrogels. Two experimental design scenarios, single- and multi-constitutive-model problems, are explored. Results show that EIG estimates can be obtained at just 8% of the full computational cost in a five-model problem within a two-dimensional design space. This advance offers a scalable path toward optimal experimental design in soft and biological materials.},
 }