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%252C+a+data-+and+compute-efficient+approach+to+developing+unified+neural+operators+for+diverse+families+of+spatiotemporal+PDEs+from+various+domains%252C+dimensions%252C+and+resolutions.+UPS+embeds+different+PDEs+into+a+shared+representation+space+and+processes+them+using+a+FNO-transformer+architecture.+Rather+than+training+the+network+from+scratch%252C+which+is+data-demanding+and+computationally+expensive%252C+we+warm-start+the+transformer+from+pretrained+LLMs+and+perform+explicit+alignment+to+reduce+the+modality+gap+while+improving+data+and+compute+efficiency.+The+cross-modal+UPS+achieves+state-of-the-art+results+on+a+wide+range+of+1D+and+2D+PDE+families+from+PDEBench%252C+outperforming+existing+unified+models+using+4+times+less+data+and+26+times+less+compute.+Meanwhile%252C+it+is+capable+of+few-shot+transfer+to+unseen+PDE+families+and+coefficients.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.08ccf8d2d9495f3cced85c12c856468c%3frik%3dQn2%252bMGkm%252bJpQBA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=PDE+Foundation+Model&FORM=IRPRST&ck=4CDDD2AC110BD169AE4860D29D1C2931&selectedIndex=15&itb=0)
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