Search Results - "Lagergren, John H"

Biologically-informed neural networks guide mechanistic modeling from sparse experimental data by Lagergren, John H, Nardini, John T, Baker, Ruth E, Simpson, Matthew J, Flores, Kevin B

“…Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying…”
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Learning partial differential equations for biological transport models from noisy spatio-temporal data by Lagergren, John H, Nardini, John T, Michael Lavigne, G, Rutter, Erica M, Flores, Kevin B

“…We investigate methods for learning partial differential equation (PDE) models from spatio-temporal data under biologically realistic levels and forms of…”
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Divide and conquer: using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods by Seethepalli, Anand, Ottley, Chanae, Childs, Joanne, Cope, Kevin R., Fine, Aubrey K., Lagergren, John H., Kalluri, Udaya, Iversen, Colleen M., York, Larry M.

“…Summary Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a…”
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Learning Equations from Biological Data with Limited Time Samples by Nardini, John T., Lagergren, John H., Hawkins-Daarud, Andrea, Curtin, Lee, Morris, Bethan, Rutter, Erica M., Swanson, Kristin R., Flores, Kevin B.

“…Equation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have…”
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Divide and conquer: using R hizo V ision E xplorer to aggregate data from multiple root scans using image concatenation and statistical methods by Seethepalli, Anand, Ottley, Chanae, Childs, Joanne, Cope, Kevin R., Fine, Aubrey K., Lagergren, John H., Kalluri, Udaya, Iversen, Colleen M., York, Larry M.

“…Summary Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a…”
Get full text

Learning partial differential equations for biological transport models from noisy spatio-temporal data by Lagergren, John H., Nardini, John T., Lavigne, G. Michael, Rutter, Erica M., Flores, Kevin B.

“…We investigate methods for learning partial differential equation (PDE) models from spatio-temporal data under biologically realistic levels and forms of…”
Get full text

Biologically-informed neural networks guide mechanistic modeling from sparse experimental data by Lagergren, John H, Nardini, John T, Baker, Ruth E, Simpson, Matthew J, Flores, Kevin B

“…Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying…”
Get full text

Learning Equations from Biological Data with Limited Time Samples by Nardini, John T, Lagergren, John H, Hawkins-Daarud, Andrea, Curtin, Lee, Morris, Bethan, Rutter, Erica M, Swanson, Kristin R, Flores, Kevin B

“…Equation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have…”
Get full text