Search Results - "Parno, Matthew"

Accounting for model form uncertainty in Bayesian calibration of linear dynamic systems by Ramancha, Mukesh K., Conte, Joel P., Parno, Matthew D.

“…Accounting for model form uncertainty is one of the key challenges in the model calibration of physical systems. It has been traditionally ignored (or not…”
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Bonded Discrete Element Simulations of Sea Ice With Non‐Local Failure: Applications to Nares Strait by West, Brendan, O’Connor, Devin, Parno, Matthew, Krackow, Max, Polashenski, Christopher

“…The discrete element method (DEM) can provide detailed descriptions of sea ice dynamics that explicitly model floes and discontinuities in the ice, which can…”
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COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications by Rowland, Michael A., Swannack, Todd M., Mayo, Michael L., Parno, Matthew, Farthing, Matthew, Dettwiller, Ian, George, Glover, England, William, Reif, Molly, Cegan, Jeffrey, Trump, Benjamin, Linkov, Igor, Lafferty, Brandon, Bridges, Todd

“…The SARS-CoV-2 virus is responsible for the novel coronavirus disease 2019 (COVID-19), which has spread to populations throughout the continental United…”
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Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework by Chrispell, John C., Jenkins, Eleanor W., Kavanagh, Kathleen R., Parno, Matthew D.

“…Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to…”
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ParticLS: Object-oriented software for discrete element methods and peridynamics by Davis, Andrew D., West, Brendan A., Frisch, Nathanael J., O’Connor, Devin T., Parno, Matthew D.

“…ParticLS ( Partic le L evel S ets) is a software library that implements the discrete element method (DEM) and meshfree methods. ParticLS tracks the…”
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High-resolution repeat topography of drifting ice floes in the Arctic Ocean from terrestrial laser scanning by Clemens-Sewall, David, Polashenski, Chris, Raphael, Ian A., Parno, Matthew, Perovich, Don, Itkin, Polona, Jaggi, Matthias, Jutila, Arttu, Macfarlane, Amy R., Matero, Ilkka S. O., Oggier, Marc, Visser, Ronald J. W., Wagner, David N.

“…Snow and ice topography impact and are impacted by fluxes of mass, energy, and momentum in Arctic sea ice. We measured the topography on approximately a 0.5 km…”
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Efficient and Exact Multimarginal Optimal Transport with Pairwise Costs by Zhou, Bohan, Parno, Matthew

“…We address the numerical solution to multimarginal optimal transport (MMOT) with pairwise costs. MMOT, as a natural extension from the classical two-marginal…”
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A probabilistic optimal sensor design approach for structural health monitoring using risk-weighted f-divergence by Yang, Yichao, Chadha, Mayank, Hu, Zhen, Vega, Manuel A., Parno, Matthew D., Todd, Michael D.

“…•The paper proposes a new form of Bayes risk functional using probability distribution distances.•This Bayes risk functional is used to design an optimal…”
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FlakeOut: A geometric approach to remove wind-blown snow from terrestrial laser scans by Clemens-Sewall, David, Parno, Matthew, Perovich, Don, Polashenski, Chris, Raphael, Ian A.

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MUQ: The MIT Uncertainty Quantification Library by Parno, Matthew, Davis, Andrew, Seelinger, Linus

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Bayesian calibration of multi-level model with unobservable distributed response and application to miter gates by Jiang, Chen, Vega, Manuel A., Ramancha, Mukesh K., Todd, Michael D., Conte, Joel P., Parno, Matthew, Hu, Zhen

“…•Bayesian calibration of multi-level model with unobservable and distributed outputs•Simultaneous model parameter estimation and model discrepancy…”
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Democratizing uncertainty quantification by Seelinger, Linus, Reinarz, Anne, Lykkegaard, Mikkel B., Akers, Robert, Alghamdi, Amal M.A., Aristoff, David, Bangerth, Wolfgang, Bénézech, Jean, Diez, Matteo, Frey, Kurt, Jakeman, John D., Jørgensen, Jakob S., Kim, Ki-Tae, Kent, Benjamin M., Martinelli, Massimiliano, Parno, Matthew, Pellegrini, Riccardo, Petra, Noemi, Riis, Nicolai A.B., Rosenfeld, Katherine, Serani, Andrea, Tamellini, Lorenzo, Villa, Umberto, Dodwell, Tim J., Scheichl, Robert

“…Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by…”
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Improved workflow for unguided multiphase image segmentation by West, Brendan A., Hodgdon, Taylor S., Parno, Matthew D., Song, Arnold J.

“…Quantitative image analysis often depends on accurate classification of pixels through a segmentation process. However, imaging artifacts such as the partial…”
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The third Sandia fracture challenge: predictions of ductile fracture in additively manufactured metal by Kramer, Sharlotte L. B., Jones, Amanda, Mostafa, Ahmed, Ravaji, Babak, Tancogne-Dejean, Thomas, Roth, Christian C., Bandpay, Maysam Gorji, Pack, Keunhwan, Foster, John T., Behzadinasab, Masoud, Sobotka, James C., McFarland, John M., Stein, Jeremy, Spear, Ashley D., Newell, Pania, Czabaj, Michael W., Williams, Bruce, Simha, Hari, Gesing, Mark, Gilkey, Lindsay N., Jones, Christopher A., Dingreville, Remi, Sanborn, Scott E., Bignell, John L., Cerrone, Albert R., Keim, Vincent, Nonn, Aida, Cooreman, Steven, Thibaux, Philippe, Ames, Nicoli, Connor, Devin O., Parno, Matthew, Davis, Brett, Tucker, Joseph, Coudrillier, Baptiste, Karlson, Kyle N., Ostien, Jakob T., Foulk, James W., Hammetter, Christopher I., Grange, Spencer, Emery, John M., Brown, Judith A., Bishop, Joseph E., Johnson, Kyle L., Ford, Kurtis R., Brinckmann, Steffen, Neilsen, Michael K., Jackiewicz, Jacek, Ravi-Chandar, K., Ivanoff, Thomas, Salzbrenner, Bradley C., Boyce, Brad L.

“…The Sandia Fracture Challenges provide a forum for the mechanics community to assess its ability to predict ductile fracture through a blind, round-robin…”
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UM-Bridge: Uncertainty quantification and modeling bridge by Seelinger, Linus, Cheng-Seelinger, Vivian, Davis, Andrew, Parno, Matthew, Reinarz, Anne

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MParT: Monotone Parameterization Toolkit by Parno, Matthew, Rubio, Paul-Baptiste, Sharp, Daniel, Brennan, Michael, Baptista, Ricardo, Bonart, Henning, Marzouk, Youssef

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Observations of Stress‐Strain in Drifting Sea Ice at Floe Scale by Parno, Julie, Polashenski, Chris, Parno, Matthew, Nelsen, Patricia, Mahoney, Andrew, Song, Arnold

“…The mechanical deformation of sea ice has substantial influence over large‐scale (e.g., >10 km) ice properties, such as the ice thickness distribution, as well…”
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Efficient and Exact Multimarginal Optimal Transport with Pairwise Costs by Zhou, Bohan, Parno, Matthew

“…In this paper, we address the numerical solution to the multimarginal optimal transport (MMOT) with pairwise costs. MMOT, as a natural extension from the…”
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A transport approach to sequential simulation-based inference by Rubio, Paul-Baptiste, Marzouk, Youssef, Parno, Matthew

“…We present a new transport-based approach to efficiently perform sequential Bayesian inference of static model parameters. The strategy is based on the…”
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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States by Cramer, Estee Y, Ray, Evan L, Lopez, Velma K, Bracher, Johannes, Brennen, Andrea, Castro Rivadeneira, Alvaro J, Gerding, Aaron, Gneiting, Tilmann, House, Katie H, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul H, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha W, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil F, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G, Tec, Mauricio, Srivastava, Ajitesh, George, Glover E, Cegan, Jeffrey C, Dettwiller, Ian D, England, William P, Farthing, Matthew W, Hunter, Robert H, Lafferty, Brandon, Linkov, Igor, Mayo, Michael L, Parno, Matthew D, Rowland, Michael A, Trump, Benjamin D, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen V, Hess, Jonathan, Morley, Christopher P, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina M, Baer, Thomas M, Eisenberg, Marisa C, Falb, Karl, Huang, Yitao, Martin, Emily T, McCauley, Ella, Myers, Robert L, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, V P, Guertin, Stephanie L, Hulme-Lowe, Christopher, Turner, Stephen D, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, van de Walle, Axel

“…Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel…”
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