About Us
Harnessing the Power of Physics-Informed AI in the Cloud
SolidNetics provides engineers and researchers a powerful simulation tool using physics-informed neural networks.
About Us
SolidNetics provides engineers and researchers a powerful simulation tool using physics-informed neural networks.
Founded in 2025, SolidNetics is redefining what's possible in engineering simulation by harnessing the power of Physics-Informed Neural Networks (PINNs). As one of the first cloud-native platforms to apply deep learning to full-scale 3D solid mechanics problems, SolidNetics is setting a new standard for accuracy, flexibility, and ease of use in simulation software.
Traditional FEA-based tools rely heavily on meshing, which can be complex, time-consuming, and error-prone—especially when dealing with intricate geometries, evolving boundaries, or discontinuities. PINNs offer a fundamentally different approach: they solve governing equations directly using neural networks, removing the need for meshing while naturally handling complex domains and boundary conditions. SolidNetics makes this cutting-edge technology accessible to engineers, designers, and researchers through a modern, web-based interface that requires no local installation.
Despite rapid growth in academic research around PINNs, industry adoption has lagged due to the absence of scalable, user-friendly tools. SolidNetics closes this gap by providing a fully integrated cloud platform where users can import CAD files, define physics-based problems, and run simulations powered by neural solvers—all within a few clicks.
SolidNetics was created by a cross-functional team of mechanical engineers, applied mathematicians, and software developers, all with a shared vision: to merge scientific innovation with practical usability. Together, we are building tools that empower the next generation of engineers to solve real-world problems faster, smarter, and more efficiently.
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.
SolidNetics is powered by a custom-built Physics-Informed Neural Network (PINN) engine developed in Python using PyTorch. The engine solves elasticity problems directly from CAD geometries without meshing, leveraging GPU acceleration for high-performance 3D simulations.
SolidNetics runs entirely on AWS cloud infrastructure to ensure scalability, reliability, and speed. Users can launch simulations from any device with no local installation.
SolidNetics uses AWS DynamoDB to manage user and project metadata, while simulation inputs and results are securely stored in AWS S3 in structured formats, including JSON and HDF5.
SolidNetics employs AWS Lambda functions for automated preprocessing tasks such as point cloud generation from CAD geometries (STL, STEP, IGES, OBJ) and normalization of geometry and boundary conditions.
Simulation tasks are managed via AWS SQS, which handles asynchronous job dispatching and ensures smooth communication between the web application and compute backends.
SolidNetics simulations run on dedicated, GPU-enabled AWS EC2 instances optimized for deep learning workloads. These "engine instances" are pre-configured with the SolidNetics runtime environment and dynamically scale based on demand.
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.
At SolidNetics, our mission is to revolutionize engineering simulation by integrating physics-informed neural networks (PINNs) with cloud computing. We aim to deliver an intelligent, scalable, and accessible simulation platform that empowers engineers and researchers to solve complex solid mechanics problems with greater accuracy, flexibility, and speed. Our platform bridges the gap between traditional simulation tools and modern AI-driven approaches, enabling seamless collaboration and innovation across disciplines.
SolidNetics envisions a future where simulation tools are not just faster or more scalable—but smarter. By combining deep learning with physics-based models in a fully cloud-native environment, we strive to make high-fidelity engineering simulation universally accessible. Our vision is to lead the transition from conventional FEA to intelligent simulation platforms that enable engineers to explore, design, and optimize the built world more efficiently and insightfully than ever before.
SolidNetics allow users to access their data and tools from anywhere with an internet connection.
Multiple users can work on the same project simultaneously, increasing efficiency and reducing errors.
SolidNetics can accommodate increased usage and data storage as the company grows.
SolidNetics eliminates the need for expensive hardware and IT infrastructure required for peridynamic analysis, reducing costs for the company.
SolidNetics provides secure storage and backup of important data, reducing the risk of data loss or theft.
SolidNetics uses powerful servers and infrastructure to ensure fast, reliable performance.
SolidNetics users receive regular software updates without any disruption, keeping the software up-to-date and secure.