# NeuralMesh for Energy AI Workloads

Energy AI workloads stall when storage cannot keep pace. NeuralMesh delivers petabyte-scale throughput, checkpoint reliability, and GPU saturation for seismic, fusion, oil, gas, and renewables.

**Type:** Solution Brief

## Key Benefits of NeuralMesh

**Compress Seismic Runtimes from Days to Hours**.

- Provide accurate seismic processing insights.
- Accelerate drilling decisions.
- Cut 3D migration cycle times.

**Feed GPUs Continuously Across Energy Simulations**.

- Maintain Full-Waveform Inversion (FWI) throughput.
- Handle Reverse Time Migration (RTM) loads.

**Protect Long-Running Simulations from Outages**.

- Shield FWI campaigns from unexpected hardware failures.
- Sustain checkpoint writes.
- Eliminate job re-spins.

**Break Reservoir Model Memory Limits**.

- Extend GPU memory into blazing fast NVMe.
- Run 10 billion cell models.
- Remove reservoir partitioning.

**Eliminate CPU and I/O Overhead Costs**.

- Write via zero-copy paths.
- Load at full hardware speeds.
- Cut NFS I/O overhead by 40%.

**Deploy On-Premises, in the Cloud, on the Edge, or Hybrid**.

- Burst to cloud for peak loads.
- Connect offshore sites to HQ.
- Unify data in one namespace.

**Serve Geoscience Teams Seamlessly on Any Protocol**.

- Deliver POSIX, NFS, SMB, S3.
- Provide a shared namespace.
- Remove seismic data sync lag.

**Tier Data Automatically at Petabyte Scale**.

- Keep hot data on NVMe.
- Recall cold archives instantly.
- Automate seismic archiving.

```json
{
  "_key": "rpqa-stin50yhs36",
  "_type": "statsBlock",
  "markDefs": null,
  "stats": [
    {
      "_key": "st0vazhvoj4w",
      "_type": "statItem",
      "label": "Drop in seismic runtime, from >80 hours to <4",
      "value": ">95%"
    },
    {
      "_key": "st1x99f0hv5r",
      "_type": "statItem",
      "label": "Increased throughput on cloud infrastructure",
      "value": "6x"
    },
    {
      "_key": "st2198ts4w0z",
      "_type": "statItem",
      "label": "Reduction in power grid study timelines",
      "value": ">80%"
    }
  ]
}
```

```json
{
  "_key": "3b4a61835fed",
  "_type": "statsBlock",
  "markDefs": null,
  "stats": [
    {
      "_key": "6d941016a37e",
      "_type": "statItem",
      "label": "Faster seismic processing performance vs. cloud",
      "value": "2x"
    },
    {
      "_key": "fe298814a6c4",
      "_type": "statItem",
      "label": "GPU utilization compared to 30% before NeuralMesh.",
      "value": ">90%"
    }
  ]
}
```

****

## Energy Industry Use Cases

**Oil & Gas**.  
NeuralMesh accelerates FWI, RTM, and reservoir simulation across petabyte-scale datasets, cutting runtimes from weeks to hours and tripling GPU utilization rates.

**Grid Operations**.  
NeuralMesh cuts interconnection study timelines, accelerating construction approval and revenue generation for RTOs and ISOs across the US and EMEA.

**Nuclear, Fusion, & Power Generation**.  
NeuralMesh sustains parallel I/O across continuous multi-physics simulation and multi-day thermal windows, no tolerance for failure.

**Renewables**.  
NeuralMesh delivers AI-powered predictive maintenance models to keep turbines and arrays online, reduce outage risks, and lift energy trading margins.

```json
{
  "_key": "9c4e2fd7ce88",
  "_type": "customerQuoteBlock",
  "markDefs": null,
  "quote": {
    "anonymizedAttribution": null,
    "anonymous": false,
    "authorName": "Dr. Rob Akers",
    "companyLogo": {
      "_type": "image",
      "asset": {
        "_id": "image-56a992b74986404c8ca813301d78b4112c399765-300x300-svg",
        "metadata": {
          "dimensions": {
            "_type": "sanity.imageDimensions",
            "aspectRatio": 1,
            "height": 300,
            "width": 300
          },
          "lqip": "data:image/png;base64,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"
        },
        "url": "https://cdn.sanity.io/images/ult5g8gw/production/56a992b74986404c8ca813301d78b4112c399765-300x300.svg"
      },
      "crop": null,
      "hotspot": null
    },
    "companyLogoMono": {
      "_type": "image",
      "asset": {
        "_id": "image-56a992b74986404c8ca813301d78b4112c399765-300x300-svg",
        "metadata": {
          "dimensions": {
            "_type": "sanity.imageDimensions",
            "aspectRatio": 1,
            "height": 300,
            "width": 300
          },
          "lqip": "data:image/png;base64,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"
        },
        "url": "https://cdn.sanity.io/images/ult5g8gw/production/56a992b74986404c8ca813301d78b4112c399765-300x300.svg"
      },
      "crop": null,
      "hotspot": null
    },
    "companyName": "UK Atomic Energy Authority",
    "jobTitle": "Director for Computing Programmes",
    "text": "By combining high-fidelity simulation with physics-informed AI to develop predictive digital twins, [we can] reduce the cost, risk, and time of learning that would otherwise require expensive and time-consuming physical testing."
  }
}
```

[Download PDF](/api/resource-pdf?slug=weka-for-energy-ai)
