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Commit 9e7e63c3 authored by Simon Labarthe's avatar Simon Labarthe
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typo correction

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...@@ -37,7 +37,7 @@ Insect pests are a major threat to agricultural systems [@oerkeCropLossesPests20 ...@@ -37,7 +37,7 @@ Insect pests are a major threat to agricultural systems [@oerkeCropLossesPests20
In the field of data assimilation for PDE and dynamic systems, existing packages provide tools to easily interface dynamic systems, observation models, and a collection of data assimilation algorithms, such as DAPPER [@raanesDAPPERDataAssimilation2024], OpenDA [@ridlerDataAssimilationFramework2014], or PDAF [@nergerPDAFPARALLELDATA2005]. In the field of data assimilation for PDE and dynamic systems, existing packages provide tools to easily interface dynamic systems, observation models, and a collection of data assimilation algorithms, such as DAPPER [@raanesDAPPERDataAssimilation2024], OpenDA [@ridlerDataAssimilationFramework2014], or PDAF [@nergerPDAFPARALLELDATA2005].
Unlike these generic packages, `Pherosensor-toolbox` is a context-specific application-oriented package specifically designed to solve the inverse problem of inferring the source term (i.e. pheromone emitters position and emission rates) within a Chemical-Transport Model (CTM) modeling pheromone propagation in an agricultural landscape. An additional feature is the possibility of informing the data assimilation with insect behavior, such as population dynamics modeled by partial differential equations (PDEs), to get Biology-Informed Data-Assimilation (BI-DA). BI-DA aims to counter-balance data scarcity with prior biological knowledge. Another upcoming feature is optimal sensor placement tools to find the most informative sensor placement for assimilating the sensor data and inferring the source term of the CTM. The target audience is then academic researchers interested in epidemio surveillance for crops. Unlike these generic packages, `Pherosensor-toolbox` is a context-specific application-oriented package specifically designed to solve the inverse problem of inferring the source term (i.e. pheromone emitters position and emission rates) within a Chemical-Transport Model (CTM) modeling pheromone propagation in an agricultural landscape. An additional feature is the possibility of informing the data assimilation with insect behavior, such as population dynamics modeled by partial differential equations (PDEs), to get Biology-Informed Data-Assimilation (BI-DA). BI-DA aims to counter-balance data scarcity with prior biological knowledge. Another upcoming feature is optimal sensor placement tools to find the most informative sensor placement for assimilating the sensor data and inferring the source term of the CTM. The target audience is then academic researchers interested in epidemic surveillance for crops.
# Outlook # Outlook
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