epanet-js
No installs. No forced cloud storage. Just fast, local-first water modeling — powered by the engine you already trust.
You shouldn't have to choose between speed, security, and affordability just to understand your water networks.


The core challenge in topology optimization is the , which consumes the majority of computation time.
The integration of into topology optimization has transformed it from a "conceptual stage" tool into a "production-ready" powerhouse . While traditional methods on CPUs often struggle with high-resolution meshes due to the sheer number of degrees of freedom, GPUs leverage parallel processing to solve these complex problems up to two orders of magnitude faster. 🚀 The Computational Leap: GPU vs. CPU
To maximize performance, researchers use specialized algorithms that align with the GPU’s SIMT (Single-Instruction-Multiple-Threads) architecture. 1. Matrix-Free Solvers
: Parallelizing these tasks on a GPU can reduce power consumption by up to 93% . 🛠️ Key GPU-Based Strategies
: GPUs use thousands of cores to perform fine-grained operations simultaneously.
EPANET was a gift to the industry — free, open-source water modeling for all. But commercial vendors built on it, locked away improvements, and left the community behind.
epanet-js is our answer: a faster, simpler, affordable water modeling tool that protects your privacy and sustains the open-source future of water modeling.
We're proud to be part of the next chapter — and we're just getting started.

When you purchase more features in epanet-js, you're investing in the future of open-source EPANET development.
Our open-source model balances innovation and accessibility:
Anyone can build on our code. The two-year commercial-use delay gives us the incentive to keep pushing forward — and that fuels progress for everyone.
That means when you support us, you support more affordable hydraulic modeling software for the entire community.
Choose the plan that works for you
Individual named license
Floating shared license
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Available for non-commercial projects, learning, and student work.
For curious minds and personal growth.
Free for students and teachers.
Find answers to common questions about epanet-js.
The core challenge in topology optimization is the , which consumes the majority of computation time.
The integration of into topology optimization has transformed it from a "conceptual stage" tool into a "production-ready" powerhouse . While traditional methods on CPUs often struggle with high-resolution meshes due to the sheer number of degrees of freedom, GPUs leverage parallel processing to solve these complex problems up to two orders of magnitude faster. 🚀 The Computational Leap: GPU vs. CPU
To maximize performance, researchers use specialized algorithms that align with the GPU’s SIMT (Single-Instruction-Multiple-Threads) architecture. 1. Matrix-Free Solvers
: Parallelizing these tasks on a GPU can reduce power consumption by up to 93% . 🛠️ Key GPU-Based Strategies
: GPUs use thousands of cores to perform fine-grained operations simultaneously.
Simple, quick, and useful right out of the gate — designed to open-and-go.
Launch epanet-js now