Browsing by Author "Viens, Frederi"

Now showing 1 - 4 of 4
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    Bayes goes fast: Uncertainty quantification for a covariant energy density functional emulated by the reduced basis method
    (Frontiers Media S.A., 2023) Giuliani, Pablo; Godbey, Kyle; Bonilla, Edgard; Viens, Frederi; Piekarewicz, Jorge
    A covariant energy density functional is calibrated using a principled Bayesian statistical framework informed by experimental binding energies and charge radii of several magic and semi-magic nuclei. The Bayesian sampling required for the calibration is enabled by the emulation of the high-fidelity model through the implementation of a reduced basis method (RBM)—a set of dimensionality reduction techniques that can speed up demanding calculations involving partial differential equations by several orders of magnitude. The RBM emulator we build—using only 100 evaluations of the high-fidelity model—is able to accurately reproduce the model calculations in tens of milliseconds on a personal computer, an increase in speed of nearly a factor of 3,300 when compared to the original solver. Besides the analysis of the posterior distribution of parameters, we present model calculations for masses and radii with properly estimated uncertainties. We also analyze the model correlation between the slope of the symmetry energy L and the neutron skin of 48Ca and 208Pb. The straightforward implementation and outstanding performance of the RBM makes it an ideal tool for assisting the nuclear theory community in providing reliable estimates with properly quantified uncertainties of physical observables. Such uncertainty quantification tools will become essential given the expected abundance of data from the recently inaugurated and future experimental and observational facilities.
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    Deriving general principles of agroecosystem multifunctionality with the Diverse Rotations Improve Valuable Ecosystem Services (DRIVES) network
    (Wiley, 2024) Bybee-Finley, K. Ann; Muller, Katherine; White, Kathryn E.; Bowles, Timothy M.; Cavigelli, Michel A.; Han, Eunjin; Schomberg, Harry H.; Snapp, Sieglinde; Viens, Frederi
    Long-term agricultural field experiments (LTFEs) have been conducted for nearly 150 years. Yet lack of coordination means that synthesis across such experiments remains rare, constituting a missed opportunity for deriving general principles of agroecosystem structure and function. Here, we introduce the Diverse Rotations Improve Valuable Ecosystem Services (DRIVES) project, which uses legacy data from North American LTFEs to address research questions about the multifunctionality of agriculture. The DRIVES Project is a network of researchers who have compiled a database of primary (i.e., observations) and secondary (i.e., transformed observations or modeling results) data from participating sites. It comprises 21 LTFEs that evaluate how crop rotational diversity impacts cropping system performance. The Network consists of United States Department of Agriculture, university, and International Maize and Wheat Improvement Center scientists (20 people) who manage and collect primary data from LTFEs and a core team (nine people) who organize the network, curate network data, and synthesize cross-network findings. As of 2024, the DRIVES Project database contains 495 site-years of crop yields, daily weather, soil analysis, and management information. The DRIVES database is findable, accessible, interoperable, and reusable, which allows integration with other public datasets. Initial research has focused on how rotational diversity impacts resilience in the face of adverse weather, nutritional quality, and economic feasibility. Our collaborative approach in handling LTFE data has established a model for data organization that facilitates broader synthesis studies. We openly invite other sites to join the DRIVES network and share their data.
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    Mathematical Modeling and Stability Analysis of Systemic Risk in the Banking Ecosystem
    (Hindawi, 2023) Irakoze, Irène; Nahayo, Fulgence; Ikpe, Dennis; Gyamerah, Samuel Asante; Viens, Frederi
    This paper investigates the dynamics of systemic risk in banking networks by analyzing equilibrium points and stability conditions. The focus is on a model that incorporates interactions among distressed and undistressed banks. The equilibrium points are determined by solving a reduced system of equations, considering both homogeneous and heterogeneous scenarios. Local and global stability analyses reveal conditions under which equilibrium points are stable or unstable. Numerical simulations further illustrate the dynamics of systemic risk, while the theoretical findings offer insights into the behavior of distressed banks under varying conditions. Overall, the model enhances our understanding of systemic financial risk and offers valuable insights for risk management and policymaking in the banking sector.
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    Rotational complexity increases cropping system output under poorer growing conditions
    (Elsevier, 2024) Bybee-Finley, K. Ann; Muller, Katherine; White, Kathryn E.; Cavigelli, Michel A.; Han, Eunjin; Schomberg, Harry H.; Snapp, Sieglinde; Viens, Frederi; Correndo, Adrian A.; Deiss, Leonardo; Fonteyne, Simon; Garcia y Garcia, Axel; Gaudin, Amélie C. M.; Hooker, David C.; Janovicek, Ken; Jin, Virginia; Johnson, Gregg; Karsten, Heather; Liebman, Matt; McDaniel, Marshall D.; Sanford, Gregg; Schmer, Marty R.; Strock, Jeffrey; Sykes, Virginia R.; Verhulst, Nele; Wilke, Brook; Bowles, Timothy M.
    Growing multiple crops in rotation can increase the sustainability of agricultural systems and reduce risks from increasingly adverse weather. However, widespread adoption of diverse rotations is limited by economic uncertainty, lack of incentives, and limited information about long-term outcomes. Here, we combined 36,000 yield observations from 20 North American long-term cropping experiments (434 site-years) to assess how greater crop diversity impacts productivity of complete rotations and their component crops under varying growing conditions. Maize and soybean output increased as the number of species and rotation length increased, while results for complete rotations varied by site depending on which crops were present. Diverse rotations reduced rotation-level output at eight sites due to the addition of lower-output crops such as small grains, illustrating trade-offs. Diverse rotations positively impacted rotation-level output under poor growing conditions, which illustrates how diverse cropping systems can reduce the risk of crop loss in a changing climate.