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What is XPS (eXtended Particle Simulations)?

XPS, which stands for eXtended Particle Simulations, is an innovative simulation tool based on the Discrete Element Method, developed by RCPE and distributed globally by InSilicoTrials, specifically designed for high-accuracy simulations of particle-based processes. This software is especially advantageous for the pharmaceutical industry, as it facilitates precise predictions of the behavior of powders and granular materials, thus providing teams with critical insights and improving the management of pharmaceutical unit operations. By employing advanced contact models, XPS effectively describes the flow dynamics of granular materials and utilizes highly parallel algorithms optimized for modern GPUs, allowing for the simulation of up to 100 million particles in a fraction of the time. The unparalleled detail offered in process configuration evaluations enables pharmaceutical engineers to explore decision-making scenarios in a virtual environment, significantly reducing the reliance on costly and time-consuming physical experiments while promoting data-centric strategies for process development. Consequently, this cutting-edge software not only enhances operational efficiency but also deepens the understanding of material behaviors in pharmaceutical manufacturing settings, fostering innovation and improving overall production processes. Additionally, XPS's capabilities pave the way for future advancements in simulation technology within the industry, ensuring that teams remain at the forefront of pharmaceutical research and development.

What is Geminus?

Geminus leverages the power of predictive intelligence by merging artificial intelligence with physical principles through cutting-edge multi-fidelity modeling techniques. Our groundbreaking AI, rooted in fundamental principles, integrates the physical constraints of reality into resilient predictive frameworks. The Geminus platform skillfully employs limited datasets to quickly assess the dynamics of complex industrial systems, facilitating accurate predictions about the impact of crucial business decisions. By fusing models with data, Geminus's multi-fidelity approach enables the rapid development of highly precise surrogates, achieving processing speeds more than 1,000 times quicker than traditional simulations. A distinctive feature of Geminus is its capability to effectively quantify model uncertainty, providing confidence in your forecasts and the strategic decisions that arise from them. Furthermore, Geminus dramatically shortens the model development timeline from several months to just a few hours, while requiring significantly less data and computational power than conventional AI or simulation methods. The models produced by Geminus are enriched with insights drawn from the actual behaviors of real-world systems, granting organizations a clearer understanding that improves decision-making. This revolutionary methodology not only optimizes the modeling process but also equips organizations with the agility to respond rapidly to evolving circumstances, ultimately enhancing their competitive edge in the marketplace.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

InSilicoTrials

Date Founded

2016

Company Location

United States

Company Website

insilicotrials.com/xps/

Company Facts

Organization Name

Geminus

Company Location

United States

Company Website

www.geminus.ai/

Categories and Features

Categories and Features

AI Translation

Not specified

Digital Twin

Not specified

Simulation

Not specified

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