T-Risk: a disaster risk calculation engine based on Tensorflow

T-Risk is a fully differentiable, GPU-accelerated disaster risk calculation engine implemented in TensorFlow. Unlike traditional risk engines that rely on object-oriented programming (OOP) structures and Central Processing Unit (CPU) execution, T-Risk leverages tensor calculus and automatic differentiation to provide high-performance risk metrics and their gradients. This architecture enables the processing of large portfolios while offering novel capabilities in sensitivity analysis, model calibration, and portfolio optimization. The software is designed to run seamlessly on modern hardware accelerators (GPUs and TPUs), bridging the gap between traditional risk assessment and the emerging ecosystem of AI-driven high-performance computing. To facilitate usability, the engine works with a Streamlit-based web interface that handles data input, real-time validation, and interactive visualization of risk metrics and sensitivity gradients.

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Publication Details

Journal
SoftwareX
Published
2026-05-16
DOI
https://doi.org/10.1016/j.softx.2026.102731
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
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article

T-Risk: a disaster risk calculation engine based on Tensorflow

Orlando Arroyo
SoftwareX
Scientific Computing and Data Management
article

T-Risk: a disaster risk calculation engine based on Tensorflow

Orlando Arroyo
article en

Abstract

T-Risk is a fully differentiable, GPU-accelerated disaster risk calculation engine implemented in TensorFlow. Unlike traditional risk engines that rely on object-oriented programming (OOP) structures and Central Processing Unit (CPU) execution, T-Risk leverages tensor calculus and automatic differentiation to provide high-performance risk metrics and their gradients. This architecture enables the processing of large portfolios while offering novel capabilities in sensitivity analysis, model calibration, and portfolio optimization. The software is designed to run seamlessly on modern hardware accelerators (GPUs and TPUs), bridging the gap between traditional risk assessment and the emerging ecosystem of AI-driven high-performance computing. To facilitate usability, the engine works with a Streamlit-based web interface that handles data input, real-time validation, and interactive visualization of risk metrics and sensitivity gradients.

SoftwareXVol. 34
Universidad Industrial de Santander (CO)
Climate action
Openalex Percentile: Top 3%
Scientific Computing and Data Management
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