Simulation Tool
Simulation Tool - Related Videos
11:29
Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
0 Views •
Cited by 15 •
2014
A new computational system featuring GPU-accelerated molecular dynamics simulation and 3D/VR visualization, analysis and manipulation of nanostructures has been implemented, representing a novel approach to advance materials research and promote innovative investigation and alternative methods to learn about material structures with dimensions invisible to the human eye.
11:05
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Cited by 10 •
2016
The following paper presents a novel FE simulation technique (KBC-FE), which reduces computational cost by performing simulations on a cloud computing environment, through the application of individual modules. Moreover, it establishes a seamless collaborative network between world leading scientists, enabling the integration of cutting edge knowledge modules into FE simulations.
06:04
Simulation of the Planetary Interior Differentiation Processes in the Laboratory
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Cited by 1 •
2013
The high-pressure and high-temperature experiments described here mimic planet interior differentiation processes. The processes are visualized and better understood by high-resolution 3D imaging and quantitative chemical analysis.
09:23
Integrating Automated Simulation Workflows with 3D Visualization for Virtual Experiments in the Metaverse
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2026
A generalized, FAIR-compliant method is presented for domain-expert researchers seeking to integrate simulation and data-processing tools into automated workflows for 3D virtual experiments. A neutronics example demonstrates setting up a local Galaxy instance, wrapping OpenMC and file-conversion tools, launching workflows from Omniverse, and visualizing the converted 3D outputs.
10:34
Designing and Implementing Nervous System Simulations on LEGO Robots
0 Views •
Cited by 7 •
2013
An approach to neural network modeling on the LEGO Mindstorms robotics platform is presented. The method provides a simulation tool for invertebrate neuroscience research in both the research lab and the classroom. This technique enables the investigation of biomimetic robot control principles.
FAQs
Simulation tools translate developmental processes into mathematical rules or algorithms that can be calculated over successive time points. These rules describe how encoded components behave under defined conditions, while parameters represent processes such as gene regulation, morphogen diffusion, cell growth, division, or movement. Changing a parameter reveals how the modeled developmental system shifts over time.
Parameters determine how the modeled components behave and interact during development. Altering them can change the simulated progression of processes such as morphogen diffusion, gene regulation, cell growth, division, or movement. Examining these changes helps researchers determine which modeled conditions produce different developmental outcomes and identify relationships that may require experimental testing.
Comparison with experimental observations connects mathematical output to biological evidence. If predicted patterns or developmental behaviors resemble observed results, the model can support a proposed mechanism; differences can reveal assumptions or relationships that need reconsideration. This process links quantitative data with biological theory and helps refine explanations of developmental processes.
A useful model requires rules or algorithms for the developmental processes being represented, together with parameters and defined conditions for calculation. Researchers then calculate how the modeled system changes over time and examine the resulting behavior. The selected components and conditions determine which developmental questions the simulation can address and how its outcomes can be interpreted.
They are particularly useful when a developmental mechanism is difficult to isolate directly in a living embryo. A simulation can represent selected processes while researchers examine how their modeled behavior changes under defined conditions. This makes the approach valuable for generating hypotheses about tissue patterning and for identifying predictions that can guide experimental design.
By modeling processes such as gene regulation, morphogen diffusion, cell growth, division, and movement, simulations can show how altered parameters influence developmental outcomes over time. Researchers can compare these predicted changes with experimental observations and use the results to investigate mechanisms associated with developmental disorders, while also guiding hypotheses and further experiments.