Scholarly Works per year
Scholarly Works per year
Fingerprint
Dive into the research topics where Mathematics is active. These topic labels come from the works of this organization's members. Together they form a unique fingerprint.
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Profiles
-
A Boundary-Field Formulation for Elastodynamic Scattering
Hsiao, G. C., Sánchez-Vizuet, T. & Wendland, W. L., Jan 2023, In: Journal of Elasticity. 153, 1, p. 5-27 23 p.Research output: Contribution to journal › Article › peer-review
-
Accounting for extra-binomial variability with differentially expressed genetic pathway data: A collaborative bioinformatic study
Aberasturi, D. T., Piegorsch, W. W., Bedrick, E. J. & Lussier, Y. A., Dec 2023, In: Stat. 12, 1, e518.Research output: Contribution to journal › Article › peer-review
-
A fast computational model for circulatory dynamics: effects of left ventricle–aorta coupling
Moulton, M. J. & Secomb, T. W., Jun 2023, In: Biomechanics and Modeling in Mechanobiology. 22, 3, p. 947-959 13 p.Research output: Contribution to journal › Article › peer-review
Open Access
-
-
-
Model-independent benchmark dose estimation for quantitative risk assessment
Project: Research project
Datasets
-
A pool-adjacent-violators-algorithm approach to detect infinite parameter estimates in one-regressor dose–response models with asymptotes
Deutsch, R. C. (Creator), Habing, B. (Creator) & Piegorsch, W. W. (Creator), figshare, 2014
DOI: 10.6084/m9.figshare.1145718.v1, https://figshare.com/articles/A_pool_adjacent_violators_algorithm_approach_to_detect_infinite_parameter_estimates_in_one_regressor_dose_8211_response_models_with_asymptotes/1145718/1
Dataset
-
Optimal parameter selection in Weeks’ method for numerical Laplace transform inversion based on machine learning
Kano, P. O. (Creator), Brio, M. (Contributor) & Bailey, J. (Creator), SAGE Journals, 2021
DOI: 10.25384/sage.c.5357908, https://sage.figshare.com/collections/Optimal_parameter_selection_in_Weeks_method_for_numerical_Laplace_transform_inversion_based_on_machine_learning/5357908
Dataset
-
sj-zip-1-act-10.1177_1748302621999621 - Supplemental material for Optimal parameter selection in Weeks’ method for numerical Laplace transform inversion based on machine learning
Kano, P. O. (Creator), Brio, M. (Contributor) & Bailey, J. (Creator), SAGE Journals, 2021
DOI: 10.25384/sage.14337082.v1, https://sage.figshare.com/articles/dataset/sj-zip-1-act-10_1177_1748302621999621_-_Supplemental_material_for_Optimal_parameter_selection_in_Weeks_method_for_numerical_Laplace_transform_inversion_based_on_machine_learning/14337082/1
Dataset