Harnessing the Power of Cloud-Native Engineering Simulation

Our Story

The Power of PINN

Physics-Informed Neural Networks (PINNs) represent a transformative approach to solving partial differential equations by embedding physical laws directly into the structure of deep neural networks. Unlike traditional finite element analysis (FEA), which relies on meshing and discrete formulations, PINNs learn continuous solutions that inherently satisfy governing equations such as equilibrium and constitutive laws. This makes them particularly powerful for problems involving complex geometries, unknown boundary conditions, evolving domains, or data-driven modeling.

PINNs bypass many of the bottlenecks that limit conventional simulation tools: they do not require meshing, can seamlessly integrate sparse experimental or field data, and are naturally suited for handling discontinuities, inverse problems, and parameter identification. However, PINNs are computationally intensive and require specialized frameworks to ensure stable and accurate solutions, especially for high-dimensional problems in 3D solid mechanics.

Over the past few years, PINNs have rapidly gained traction in the research community, with growing literature demonstrating their ability to solve problems that challenge or exceed the capabilities of traditional numerical methods. Despite their promise, industrial adoption has remained limited due to the absence of scalable, production-grade platforms capable of handling real-world 3D physics with the reliability demanded by engineers.

SolidNetics fills this gap by offering a cloud-native simulation platform built specifically for 3D solid mechanics using PINNs. It enables engineers to perform stress analysis directly on CAD models without meshing, define boundary conditions visually, and run high-performance simulations powered by modern GPU-accelerated cloud infrastructure.

With SolidNetics, the power of PINNs becomes practical—accessible from any device, scalable on demand, and capable of handling the complexity of modern engineering systems.

The Power of Peridynamics

Peridynamics offers a nonlocal reformulation of continuum mechanics that naturally captures fracture, discontinuities, and material failure without requiring crack tracking or re-meshing. Unlike classical methods that depend on spatial derivatives, Peridynamics evaluates interactions over finite distances, making it inherently well-suited for problems involving damage evolution, crack branching, fragmentation, and high-velocity impact.

This capability provides a major advantage in scenarios where classical FEA or mesh-based methods become unstable or inaccurate, such as dynamic fracture, composite materials, or impact-driven failure. Peridynamics handles these complexities by construction, enabling predictive simulation of failure mechanisms that are traditionally difficult to capture.

SolidNetics integrates Peridynamics into a fully automated cloud environment. Users can import CAD geometry, define contact and material models, and run simulations on scalable compute resources—without writing code or building custom solvers. This brings advanced fracture and impact modeling into practical engineering workflows for the first time.

With SolidNetics, the power of Peridynamics becomes accessible and production-ready, delivering robust failure modeling for real-world engineering applications.

The Power of Cloud FEA

Finite Element Analysis (FEA) remains a foundational tool for engineering simulation, providing proven reliability, accuracy, and performance across a wide range of structural and thermal problems. However, traditional desktop FEA tools often require complex installations, manual meshing workflows, and significant computing hardware to handle large models or high-fidelity simulations.

Cloud-based FEA removes these constraints by separating model preparation from computation. SolidNetics combines automated meshing pipelines with state-of-the-art FEA solvers, allowing users to run large, complex analyses directly from the browser on scalable CPU and GPU infrastructure. This eliminates hardware limitations and accelerates turnaround times.

With integrated CAD handling, boundary-condition assignment, visualization, and high-performance computing, SolidNetics brings the strengths of classical FEA into a modern cloud ecosystem. Engineers gain access to reliable numerical methods with greater speed, flexibility, and scalability—all without managing software, licenses, or hardware.

Our Team

SolidNetics was founded by a diverse and forward-thinking team of mechanical engineers with deep expertise in computational solid mechanics, machine learning and AI engineers specializing in physics-informed neural networks, computer scientists, and interdisciplinary researchers. Our mission is to merge physics-based simulation with modern AI, cloud computing, and software product development to redefine how engineering problems are solved.

We are actively expanding our team and invite engineers, researchers, and professionals from the fields of solid mechanics, machine learning, AI, software engineering, and cloud infrastructure to join us. At SolidNetics, you'll be part of a collaborative and innovative environment where cutting-edge simulation tools and cloud-based technologies are developed to empower the next generation of engineering analysis.